badbad

.gitignore

@@ -1 +1,2 @@
-**.pyc
+__pycache__
+__ignore*

CLAUDE.md

@@ -0,0 +1,91 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+This is **TCID** (Transformer CI Dashboard) - a Gradio-based web dashboard that displays test results for Transformer models across AMD and NVIDIA hardware. The application fetches CI test data from HuggingFace datasets and presents it through interactive visualizations and detailed failure reports.
+
+## Architecture
+
+### Core Components
+
+- **`app.py`** - Main Gradio application with UI components, plotting functions, and data visualization logic
+- **`data.py`** - Data fetching module that retrieves test results from HuggingFace datasets for AMD and NVIDIA CI runs
+- **`styles.css`** - Complete dark theme styling for the Gradio interface
+- **`requirements.txt`** - Python dependencies (matplotlib only)
+
+### Data Flow
+
+1. **Data Loading**: `get_data()` in `data.py` fetches latest CI results from:
+   - AMD: `hf://datasets/optimum-amd/transformers_daily_ci`
+   - NVIDIA: `hf://datasets/hf-internal-testing/transformers_daily_ci`
+
+2. **Data Processing**: Results are joined and filtered to show only important models defined in `IMPORTANT_MODELS` list
+
+3. **Visualization**: Two main views:
+   - **Summary Page**: Horizontal bar charts showing test results for all models
+   - **Detail View**: Pie charts for individual models with failure details
+
+### UI Architecture
+
+- **Sidebar**: Model selection, refresh controls, CI job links
+- **Main Content**: Dynamic display switching between summary and detail views
+- **Auto-refresh**: Data reloads every 15 minutes via background threading
+
+## Running the Application
+
+### Development Commands
+
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the application
+python app.py
+```
+
+### HuggingFace Spaces Deployment
+
+This application is configured for HuggingFace Spaces deployment:
+- **Framework**: Gradio 5.38.0
+- **App file**: `app.py`
+- **Configuration**: See `README.md` header for Spaces metadata
+
+## Key Data Structures
+
+### Model Results DataFrame
+The joined DataFrame contains these columns:
+- `success_amd` / `success_nvidia` - Number of passing tests
+- `failed_multi_no_amd` / `failed_multi_no_nvidia` - Multi-GPU failure counts
+- `failed_single_no_amd` / `failed_single_no_nvidia` - Single-GPU failure counts
+- `failures_amd` / `failures_nvidia` - Detailed failure information objects
+- `job_link_amd` / `job_link_nvidia` - CI job URLs
+
+### Important Models List
+Predefined list in `data.py` focusing on significant models:
+- Classic models: bert, gpt2, t5, vit, clip, whisper
+- Modern models: llama, gemma3, qwen2, mistral3
+- Multimodal: qwen2_5_vl, llava, smolvlm, internvl
+
+## Styling and Theming
+
+The application uses a comprehensive dark theme with:
+- Fixed sidebar layout (300px width)
+- Black background throughout (`#000000`)
+- Custom scrollbars with dark styling
+- Monospace fonts for technical aesthetics
+- Gradient buttons and hover effects
+
+## Error Handling
+
+- **Data Loading Failures**: Falls back to predefined model list for testing
+- **Missing Model Data**: Shows "No data available" message in visualizations
+- **Empty Results**: Gracefully handles cases with no test results
+
+## Performance Considerations
+
+- **Memory Management**: Matplotlib configured to prevent memory warnings
+- **Interactive Mode**: Disabled to prevent figure accumulation
+- **Auto-reload**: Background threading with daemon timers
+- **Data Caching**: Global variables store loaded data between UI updates

README.md

@@ -1,13 +1,43 @@
 ---
-title: Performative Dashboard
-emoji: 📈
-colorFrom: yellow
-colorTo: green
+title: Tcid
+emoji: 👁
+colorFrom: indigo
+colorTo: pink
 sdk: gradio
-sdk_version: 5.47.1
+sdk_version: 5.38.0
 app_file: app.py
 pinned: false
-short_description: damn i love matcha
+short_description: A dashboard
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# TCID
+
+This space displays the state of the `transformers` CI on two hardwares, for a subset **important models**. The CI is run daily, on both AMD MI325 and Nvidia A10. The CI runs a different number of tests for each model. When a test finishes, it is assigned a status depending on its outcome:
+
+- passed: the test finsihed and the expected output (or outputs) were retrieved;
+- failed: the test either did not finish or the output was different from the expected output;
+- skipped: the test was not run, but it not expected to. More details on this at the end of the README;
+- error: the test did not finish and python crashed;
+
+The dashboard is divided in two main parts:
+
+## Summary page
+
+On the summary page, you can see a snapshot of the mix of test passed, failed and skipped for each model. The summary page also features an "Overall failures rate" for AMD and NVIDIA, which is computed this way:
+```overall_failure_rate = (failed + error) / (passed + failed + error)``` 
+
+We do not account for the test skipped in this overall failure rate, because skipped test have no chance to neither pass nor fail.  
+We only consider the tests for a **subset of models** out of all the models supported in `transformers`. This subset is named important models, and is mainly defined by model usage.
+
+## Models page
+
+From the sidebar, you can access a detailled view of each model. In it, you will find the breakdown of test statuses and the names of the test that failed for single and multi-gpu runs.
+
+## Skipped test
+
+You can probably see many skipped tests in the `transformers` CI, which be perplexing. When a test is skipped, it's usually one of three reasons:
+- the test requires a package that is not included in the default transformers docker that the CI uses, like flash attention 3 or deepspeed;
+- the hardware is not the correct one, for instance there are a bunch of MPS (apple hardware) tests that are of course not run on AMD or Nvidia CI;
+- the model is incompatible with what the test is for, say torch.fx or flash-attention, which are incompatible with some models architecture;
+
+Skipping tests rather than not collecting them offers the advantage of having similar test counts across CIs that do not run on the same hardware. Thus, it total test count differs between two CIs, one can immediately know that one of the two only ran partialy. This would not be the case if some skipped tests were not collected at all.

app.py

@@ -1,17 +1,184 @@
+import matplotlib.pyplot as plt
+import matplotlib
+import pandas as pd
 import gradio as gr
-from grid_search_tab import create_grid_search_tab
-from theme_config import DashboardTheme
 
-def create_dashboard():
-    DashboardTheme.setup_matplotlib_style()
-    with gr.Blocks(title="Performance Dashboard", theme=DashboardTheme.get_gradio_theme()) as demo:
-        with gr.Tabs():
+from data import CIResults
+from utils import logger
+from summary_page import create_summary_page
 
-            with gr.TabItem("Grid search benchmark"):
-                create_grid_search_tab()
 
-    return demo
+# Configure matplotlib to prevent memory warnings and set dark background
+matplotlib.rcParams['figure.facecolor'] = '#000000'
+matplotlib.rcParams['axes.facecolor'] = '#000000'
+matplotlib.rcParams['savefig.facecolor'] = '#000000'
+plt.ioff()  # Turn off interactive mode to prevent figure accumulation
 
+
+# Load data once at startup
+Ci_results = CIResults()
+Ci_results.load_data()
+# Start the auto-reload scheduler
+Ci_results.schedule_data_reload()
+
+
+# Function to get current description text
+def get_description_text():
+    """Get description text with integrated last update time."""
+    msg = [
+        "Transformer CI Dashboard",
+        "-",
+        "AMD runs on MI325",
+        "NVIDIA runs on A10",
+    ]
+    msg = ["**" + x + "**" for x in msg] + [""]
+    if Ci_results.latest_update_msg:
+        msg.append(f"*This dashboard only tracks important models*<br>*({Ci_results.latest_update_msg})*")
+    else:
+        msg.append("*This dashboard only tracks important models*<br>*(loading...)*")
+    return "<br>".join(msg)
+
+# Load CSS from external file
+def load_css():
+    try:
+        with open("styles.css", "r") as f:
+            css_content = f.read()
+        
+        return css_content
+    except FileNotFoundError:
+        logger.warning("styles.css not found, using minimal default styles")
+        return "body { background: #000; color: #fff; }"
+
+
+# Create the Gradio interface with sidebar and dark theme
+with gr.Blocks(title="Model Test Results Dashboard", css=load_css(), fill_height=True, fill_width=True) as demo:
+
+
+    with gr.Row():
+        # Sidebar for model selection
+        with gr.Column(scale=1, elem_classes=["sidebar"]):
+            gr.Markdown("# 🤖 TCID", elem_classes=["sidebar-title"])
+
+            # Description with integrated last update time
+            description_text = get_description_text()
+            description_display = gr.Markdown(description_text, elem_classes=["sidebar-description"])
+
+            # Summary button
+            summary_button = gr.Button(
+                "summary\n📊",
+                variant="primary",
+                size="lg",
+                elem_classes=["summary-button"]
+            )
+
+            # CI job links at bottom of sidebar
+            ci_links_display = gr.Markdown("🔗 **CI Jobs:** *Loading...*", elem_classes=["sidebar-links"])
+
+        # Main content area
+        with gr.Column(scale=4, elem_classes=["main-content"]):
+            # Summary display (default view)
+            summary_display = gr.ScatterPlot(
+                pd.DataFrame({
+                    "x": [i for i in range(10)] + [100, -100],
+                    "y": [i ** 2 for i in range(10)] + [100, -100],
+                }),
+                x = "x",
+                y = "y",
+                height="100vh",
+                container=False,
+                show_fullscreen_button=True,
+                elem_classes=["plot-container"],
+            )
+
+
+
+    # Summary button click handler
+    def show_summary_and_update_links():
+        """Show summary page and update CI links."""
+        return create_summary_page(Ci_results.df, Ci_results.available_models), get_description_text(), get_ci_links()
+
+    summary_button.click(
+        fn=show_summary_and_update_links,
+        outputs=[summary_display, description_display, ci_links_display]
+    )
+
+    # Function to get CI job links
+    def get_ci_links():
+        """Get CI job links from the most recent data."""
+        try:
+            # Check if df exists and is not empty
+            if Ci_results.df is None or Ci_results.df.empty:
+                return "🔗 **CI Jobs:** *Loading...*"
+
+            # Get links from any available model (they should be the same for all models in a run)
+            amd_multi_link = None
+            amd_single_link = None
+            nvidia_multi_link = None
+            nvidia_single_link = None
+
+            for model_name in Ci_results.df.index:
+                row = Ci_results.df.loc[model_name]
+
+                # Extract AMD links
+                if pd.notna(row.get('job_link_amd')) and (not amd_multi_link or not amd_single_link):
+                    amd_link_raw = row.get('job_link_amd')
+                    if isinstance(amd_link_raw, dict):
+                        if 'multi' in amd_link_raw and not amd_multi_link:
+                            amd_multi_link = amd_link_raw['multi']
+                        if 'single' in amd_link_raw and not amd_single_link:
+                            amd_single_link = amd_link_raw['single']
+
+                # Extract NVIDIA links
+                if pd.notna(row.get('job_link_nvidia')) and (not nvidia_multi_link or not nvidia_single_link):
+                    nvidia_link_raw = row.get('job_link_nvidia')
+                    if isinstance(nvidia_link_raw, dict):
+                        if 'multi' in nvidia_link_raw and not nvidia_multi_link:
+                            nvidia_multi_link = nvidia_link_raw['multi']
+                        if 'single' in nvidia_link_raw and not nvidia_single_link:
+                            nvidia_single_link = nvidia_link_raw['single']
+
+                # Break if we have all links
+                if amd_multi_link and amd_single_link and nvidia_multi_link and nvidia_single_link:
+                    break
+
+
+            # Add FAQ link at the bottom
+            links_md = "❓ [**FAQ**](https://huggingface.co/spaces/transformers-community/transformers-ci-dashboard/blob/main/README.md)\n\n"
+            links_md += "🔗 **CI Jobs:**\n\n"
+
+            # AMD links
+            if amd_multi_link or amd_single_link:
+                links_md += "**AMD:**\n"
+                if amd_multi_link:
+                    links_md += f"• [Multi GPU]({amd_multi_link})\n"
+                if amd_single_link:
+                    links_md += f"• [Single GPU]({amd_single_link})\n"
+                links_md += "\n"
+
+            # NVIDIA links
+            if nvidia_multi_link or nvidia_single_link:
+                links_md += "**NVIDIA:**\n"
+                if nvidia_multi_link:
+                    links_md += f"• [Multi GPU]({nvidia_multi_link})\n"
+                if nvidia_single_link:
+                    links_md += f"• [Single GPU]({nvidia_single_link})\n"
+
+            if not (amd_multi_link or amd_single_link or nvidia_multi_link or nvidia_single_link):
+                links_md += "*No links available*"
+
+            return links_md
+        except Exception as e:
+            logger.error(f"getting CI links: {e}")
+            return "🔗 **CI Jobs:** *Error loading links*\n\n❓ **[FAQ](README.md)**"
+
+
+    # Auto-update CI links when the interface loads
+    demo.load(
+        fn=get_ci_links,
+        outputs=[ci_links_display]
+    )
+
+
+# Gradio entrypoint
 if __name__ == "__main__":
-    app = create_dashboard()
-    app.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch()

data.py

@@ -0,0 +1,227 @@
+from huggingface_hub import HfFileSystem
+import pandas as pd
+from utils import logger
+import threading
+import traceback
+import json
+import re
+
+# NOTE: if caching is an issue, try adding `use_listings_cache=False`
+fs = HfFileSystem()
+
+IMPORTANT_MODELS = [
+    "auto",
+    "bert",  # old but dominant (encoder only)
+    "gpt2",  # old (decoder)
+    "t5",  # old (encoder-decoder)
+    "modernbert",  # (encoder only)
+    "vit",  # old (vision) - fixed comma
+    "clip",  # old but dominant (vision)
+    "detr",  # objection detection, segmentation (vision)
+    "table_transformer",  # objection detection (visioin) - maybe just detr?
+    "got_ocr2",  # ocr (vision)
+    "whisper",  # old but dominant (audio)
+    "wav2vec2",  # old (audio)
+    "qwen2_audio",  # (audio)
+    "speech_t5",  # (audio)
+    "csm",  # (audio)
+    "llama",  # new and dominant (meta)
+    "gemma3",  # new (google)
+    "qwen2",  # new (Alibaba)
+    "mistral3",  # new (Mistral) - added missing comma
+    "qwen2_5_vl",  # new (vision)
+    "llava",  # many models from it (vision)
+    "smolvlm",  # new (video)
+    "internvl",  # new (video)
+    "gemma3n",  # new (omnimodal models)
+    "qwen2_5_omni",  # new (omnimodal models)
+    # "gpt_oss",  # new (quite used)
+    "qwen2_5_omni",  # new (omnimodal models)
+]
+
+KEYS_TO_KEEP = [
+    "success_amd",
+    "success_nvidia",
+    "skipped_amd",
+    "skipped_nvidia",
+    "failed_multi_no_amd",
+    "failed_multi_no_nvidia",
+    "failed_single_no_amd",
+    "failed_single_no_nvidia",
+    "failures_amd",
+    "failures_nvidia",
+    "job_link_amd",
+    "job_link_nvidia",
+]
+
+
+def log_dataframe_link(link: str) -> str:
+    """
+    Adds the link to the dataset in the logs, modifies it to get a clockable link and then returns the date of the 
+    report.
+    """
+    logger.info(f"Reading df located at {link}")
+    # Make sure the links starts with an http adress
+    if link.startswith("hf://"):
+        link = "https://huggingface.co/" + link.removeprefix("hf://")
+    # Pattern to match transformers_daily_ci followed by any path, then a date (YYYY-MM-DD format)
+    pattern = r'transformers_daily_ci(.*?)/(\d{4}-\d{2}-\d{2})'
+    match = re.search(pattern, link)
+    # Failure case: 
+    if not match:
+        logger.error("Could not find transformers_daily_ci and.or date in the link")
+        return "9999-99-99"
+    # Replace the path between with blob/main
+    path_between = match.group(1)
+    link = link.replace("transformers_daily_ci" + path_between, "transformers_daily_ci/blob/main")
+    logger.info(f"Link to data source: {link}")
+    # Return the date
+    return match.group(2)
+
+def infer_latest_update_msg(date_df_amd: str, date_df_nvidia: str) -> str:
+    # Early return if one of the dates is invalid
+    if date_df_amd.startswith("9999") and date_df_nvidia.startswith("9999"):
+        return "could not find last update time"
+    # Warn if dates are not the same
+    if date_df_amd != date_df_nvidia:
+        logger.warning(f"Different dates found: {date_df_amd} (AMD) vs {date_df_nvidia} (NVIDIA)")
+    # Take the latest date and format it
+    try:
+        latest_date = max(date_df_amd, date_df_nvidia)
+        yyyy, mm, dd = latest_date.split("-")
+        return f"last updated {mm}/{dd}/{yyyy}"
+    except Exception as e:
+        logger.error(f"When trying to infer latest date, got error {e}")
+        return "could not find last update time"
+
+def read_one_dataframe(json_path: str, device_label: str) -> tuple[pd.DataFrame, str]:
+    df_upload_date = log_dataframe_link(json_path)
+    df = pd.read_json(json_path, orient="index")
+    df.index.name = "model_name"
+    df[f"failed_multi_no_{device_label}"] = df["failures"].apply(lambda x: len(x["multi"]) if "multi" in x else 0)
+    df[f"failed_single_no_{device_label}"] = df["failures"].apply(lambda x: len(x["single"]) if "single" in x else 0)
+    return df, df_upload_date
+
+def get_first_working_df(file_list: list[str]) -> str:
+    for file in file_list:
+        job_links = file.rsplit('/', 1)[0] + "/job_links.json"
+        try:
+            links = pd.read_json(f"hf://{job_links}", typ="series")
+            has_one_working_link = any(links.values)
+        except Exception as e:
+            logger.error(f"Could not read job links from {job_links}: {e}")
+            has_one_working_link = False
+        if has_one_working_link:
+            return file
+        logger.warning(f"Skipping {file} as it has no working job links.")
+    raise RuntimeError("Could not find any working dataframe in the provided list.")
+
+
+def get_sample_data() -> tuple[pd.DataFrame, str]:
+    # Retrieve sample dataframes
+    df_amd, _ = read_one_dataframe("sample_amd.json", "amd")
+    df_nvidia, _ = read_one_dataframe("sample_nvidia.json", "nvidia")
+    # Join both dataframes
+    joined = df_amd.join(df_nvidia, rsuffix="_nvidia", lsuffix="_amd", how="outer")
+    joined = joined[KEYS_TO_KEEP]
+    joined.index = joined.index.str.replace("^models_", "", regex=True)
+    # Fitler out all but important models
+    important_models_lower = [model.lower() for model in IMPORTANT_MODELS]
+    filtered_joined = joined[joined.index.str.lower().isin(important_models_lower)]
+    # Prefix all model names with "sample_"
+    filtered_joined.index = "sample_" + filtered_joined.index
+    return filtered_joined, "sample data was loaded"
+
+def safe_extract(row: pd.DataFrame, key: str) -> int:
+    return int(row.get(key, 0)) if pd.notna(row.get(key, 0)) else 0
+
+def extract_model_data(row: pd.Series) -> tuple[dict[str, int], dict[str, int], int, int, int, int]:
+    """Extract and process model data from DataFrame row."""
+    # Handle missing values and get counts directly from dataframe
+    success_nvidia = safe_extract(row, "success_nvidia")
+    success_amd = safe_extract(row, "success_amd")
+
+    skipped_nvidia = safe_extract(row, "skipped_nvidia")
+    skipped_amd = safe_extract(row, "skipped_amd")
+    
+    failed_multi_amd = safe_extract(row, 'failed_multi_no_amd')
+    failed_multi_nvidia = safe_extract(row, 'failed_multi_no_nvidia')
+    failed_single_amd = safe_extract(row, 'failed_single_no_amd')
+    failed_single_nvidia = safe_extract(row, 'failed_single_no_nvidia')
+    # Calculate total failures
+    total_failed_amd = failed_multi_amd + failed_single_amd
+    total_failed_nvidia = failed_multi_nvidia + failed_single_nvidia
+    # Create stats dictionaries directly from dataframe values
+    amd_stats = {
+        'passed': success_amd,
+        'failed': total_failed_amd,
+        'skipped': skipped_amd,
+        'error': 0     # Not available in this dataset
+    }
+    nvidia_stats = {
+        'passed': success_nvidia,
+        'failed': total_failed_nvidia,
+        'skipped': skipped_nvidia,
+        'error': 0     # Not available in this dataset
+    }
+    return amd_stats, nvidia_stats, failed_multi_amd, failed_single_amd, failed_multi_nvidia, failed_single_nvidia
+
+
+
+class CIResults:
+
+    def __init__(self):
+        self.df = pd.DataFrame()
+        self.available_models = []
+        self.latest_update_msg = ""
+
+    def load_data(self) -> None:
+        """Load data from the data source."""
+        # Try loading the distant data, and fall back on sample data for local tinkering
+
+        error_msg = [
+            "Loading data failed:",
+            "-" * 120,
+            traceback.format_exc(),
+            "-" * 120,
+            "Falling back on sample data."
+        ]
+        logger.error("\n".join(error_msg))
+        new_df, latest_update_msg = get_sample_data()
+        self.latest_update_msg = latest_update_msg
+
+        # Update attributes
+        self.df = new_df
+        self.available_models = new_df.index.tolist()
+        # Log and return distant load status
+        logger.info(f"Data loaded successfully: {len(self.available_models)} models")
+        logger.info(f"Models: {self.available_models[:5]}{'...' if len(self.available_models) > 5 else ''}")
+        logger.info(f"Latest update message: {self.latest_update_msg}")
+        # Log a preview of the df
+        msg = {}
+        for model in self.available_models[:3]:
+            msg[model] = {}
+            for col in self.df.columns:
+                value = self.df.loc[model, col]
+                if not isinstance(value, int):
+                    value = str(value)
+                    if len(value) > 10:
+                        value = value[:10] + "..."
+                msg[model][col] = value
+        logger.info(json.dumps(msg, indent=4))
+
+    def schedule_data_reload(self):
+        """Schedule the next data reload."""
+        def reload_data():
+            self.load_data()
+            # Schedule the next reload in 15 minutes (900 seconds)
+            timer = threading.Timer(900.0, reload_data)
+            timer.daemon = True  # Dies when main thread dies
+            timer.start()
+            logger.info("Next data reload scheduled in 15 minutes")
+
+        # Start the first reload timer
+        timer = threading.Timer(900.0, reload_data)
+        timer.daemon = True
+        timer.start()
+        logger.info("Data auto-reload scheduled every 15 minutes")

grid_search_tab.py

@@ -1,117 +0,0 @@
-import gradio as gr
-import matplotlib.pyplot as plt
-import numpy as np
-from datetime import datetime, timedelta
-from theme_config import DashboardTheme
-
-def generate_sales_chart(chart_type, time_period, product_filter, primary_color, secondary_color):
-    colors = DashboardTheme.get_chart_colors()
-    fig, ax = plt.subplots(figsize=(10, 6))
-
-    # Generate dummy data based on time period
-    if time_period == "Last 7 days":
-        days = 7
-    elif time_period == "Last 30 days":
-        days = 30
-    else:  # Last 90 days
-        days = 90
-
-    dates = [(datetime.now() - timedelta(days=i)) for i in range(days, 0, -1)]
-
-    if chart_type == "Revenue":
-        # Generate dummy revenue data
-        revenue = np.random.randint(1000, 5000, days) + np.sin(np.arange(days)) * 500
-        ax.plot(dates, revenue, marker='o', linewidth=2.5, markersize=5, color=primary_color)
-        ax.set_ylabel('Revenue ($)')
-        ax.set_title(f'Sales Revenue - {product_filter} ({time_period})')
-
-    elif chart_type == "Units Sold":
-        # Generate dummy units data
-        units = np.random.randint(50, 200, days) + np.cos(np.arange(days)) * 20
-        ax.bar(dates, units, alpha=0.8, color=primary_color, edgecolor=secondary_color, linewidth=0.5)
-        ax.set_ylabel('Units Sold')
-        ax.set_title(f'Units Sold - {product_filter} ({time_period})')
-
-    else:  # Conversion Rate
-        # Generate dummy conversion rate data
-        conversion = np.random.uniform(2, 8, days) + np.sin(np.arange(days) * 0.3) * 1
-        ax.plot(dates, conversion, marker='s', linewidth=2.5, markersize=5, color=primary_color)
-        ax.set_ylabel('Conversion Rate (%)')
-        ax.set_title(f'Conversion Rate - {product_filter} ({time_period})')
-
-    ax.set_xlabel('Date')
-    ax.grid(True, alpha=0.3)
-    plt.xticks(rotation=45)
-    plt.tight_layout()
-
-    # Apply consistent styling
-    ax.spines['top'].set_visible(False)
-    ax.spines['right'].set_visible(False)
-    ax.tick_params(colors=DashboardTheme.LIGHT_GREY)
-    ax.set_facecolor(DashboardTheme.WHITE)
-
-    return fig
-
-def create_grid_search_tab():
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### Sales Dashboard Options")
-
-            chart_type = gr.Radio(
-                choices=["Revenue", "Units Sold", "Conversion Rate"],
-                value="Revenue",
-                label="Chart Type"
-            )
-
-            time_period = gr.Dropdown(
-                choices=["Last 7 days", "Last 30 days", "Last 90 days"],
-                value="Last 30 days",
-                label="Time Period"
-            )
-
-            product_filter = gr.Dropdown(
-                choices=["All Products", "Electronics", "Clothing", "Books", "Home & Garden"],
-                value="All Products",
-                label="Product Category"
-            )
-
-            region_filter = gr.CheckboxGroup(
-                choices=["North America", "Europe", "Asia", "South America"],
-                value=["North America", "Europe"],
-                label="Regions"
-            )
-
-            gr.Markdown("#### Color Options")
-            primary_color = gr.ColorPicker(
-                value="#2C3E50",
-                label="Primary Color"
-            )
-            secondary_color = gr.ColorPicker(
-                value="#7F8C8D",
-                label="Secondary Color"
-            )
-
-            update_btn = gr.Button("Update Chart", variant="primary")
-
-        with gr.Column(scale=3):
-            plot = gr.Plot(label="Sales Analytics")
-
-    # Update plot when inputs change
-    inputs = [chart_type, time_period, product_filter, primary_color, secondary_color]
-
-    update_btn.click(
-        fn=generate_sales_chart,
-        inputs=inputs,
-        outputs=plot
-    )
-
-    # Initialize with default plot
-    chart_type.change(
-        fn=generate_sales_chart,
-        inputs=inputs,
-        outputs=plot
-    )
-
-    # Set initial plot
-    demo_plot = generate_sales_chart("Revenue", "Last 30 days", "All Products", "#2C3E50", "#7F8C8D")
-    plot.value = demo_plot

requirements.txt

@@ -1,3 +1 @@
-gradio>=4.0.0
-matplotlib>=3.5.0
-numpy>=1.21.0
+matplotlib>=3.8

sample_amd.json

@@ -0,0 +1,1839 @@
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+                    "trace": "(line 715)  AssertionError: Lists differ: ['Wri[57 chars]s, a silent stream,\\nInto the neural net, a wa[42 chars],\\n'] != ['Wri[57 chars]s, a river deep,\\nWith patterns hidden, secret[46 chars]ing']"
+                },
+                {
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+                    "trace": "(line 715)  AssertionError: Lists differ: ['use[114 chars]rown cow standing on a sandy beach with clear [264 chars]cow\"] != ['use[114 chars]rown and white cow standing on a sandy beach n[272 chars]ach']"
+                },
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+                    "trace": "(line 715)  AssertionError: Lists differ: [\"user\\nYou are a helpful assistant.\\n\\nHe[678 chars]h a'] != ['user\\nYou are a helpful assistant.\\n\\nHe[658 chars]h a']"
+                },
+                {
+                    "line": "tests/models/gemma3/test_modeling_gemma3.py::Gemma3IntegrationTest::test_model_4b_bf16",
+                    "trace": "(line 715)  AssertionError: Lists differ: ['use[114 chars]rown cow standing on a sandy beach with clear [55 chars]ike'] != ['use[114 chars]rown and white cow standing on a sandy beach w[68 chars]oks']"
+                },
+                {
+                    "line": "tests/models/gemma3/test_modeling_gemma3.py::Gemma3IntegrationTest::test_model_4b_crops",
+                    "trace": "(line 715)  AssertionError: Lists differ: [\"use[251 chars]. There's a blue sky with some white clouds in the background\"] != [\"use[251 chars]. There's a bright blue sky with some white clouds in the\"]"
+                },
+                {
+                    "line": "tests/models/gemma3/test_modeling_gemma3.py::Gemma3IntegrationTest::test_model_4b_multiimage",
+                    "trace": "(line 715)  AssertionError: Lists differ: [\"use[122 chars]n\\n**Main Features:**\\n\\n*   **Chinese Archway[19 chars]ent\"] != [\"use[122 chars]n\\n**Overall Scene:**\\n\\nIt looks like a stree[18 chars]nt,\"]"
+                }
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+            "multi": [
+                {
+                    "line": "tests/models/gemma3/test_modeling_gemma3.py::Gemma3Vision2TextModelTest::test_model_parallelism",
+                    "trace": "(line 925)  RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:1 and cuda:0!"
+                },
+                {
+                    "line": "tests/models/gemma3/test_modeling_gemma3.py::Gemma3IntegrationTest::test_model_1b_text_only",
+                    "trace": "(line 715)  AssertionError: Lists differ: ['Wri[57 chars]s, a silent stream,\\nInto the neural net, a wa[42 chars],\\n'] != ['Wri[57 chars]s, a river deep,\\nWith patterns hidden, secret[46 chars]ing']"
+                },
+                {
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+                    "trace": "(line 715)  AssertionError: Lists differ: ['use[114 chars]rown cow standing on a sandy beach with clear [264 chars]cow\"] != ['use[114 chars]rown and white cow standing on a sandy beach n[272 chars]ach']"
+                },
+                {
+                    "line": "tests/models/gemma3/test_modeling_gemma3.py::Gemma3IntegrationTest::test_model_4b_batch_crops",
+                    "trace": "(line 715)  AssertionError: Lists differ: [\"user\\nYou are a helpful assistant.\\n\\nHe[678 chars]h a'] != ['user\\nYou are a helpful assistant.\\n\\nHe[658 chars]h a']"
+                },
+                {
+                    "line": "tests/models/gemma3/test_modeling_gemma3.py::Gemma3IntegrationTest::test_model_4b_bf16",
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+                },
+                {
+                    "line": "tests/models/gemma3/test_modeling_gemma3.py::Gemma3IntegrationTest::test_model_4b_multiimage",
+                    "trace": "(line 715)  AssertionError: Lists differ: [\"use[122 chars]n\\n**Main Features:**\\n\\n*   **Chinese Archway[19 chars]ent\"] != [\"use[122 chars]n\\n**Overall Scene:**\\n\\nIt looks like a stree[18 chars]nt,\"]"
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+                }
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+                }
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+                    "trace": "(line 715)  AssertionError: Lists differ: ['My [35 chars], organic, gluten free, vegan, and vegetarian. I love to use'] != ['My [35 chars], organic, gluten free, vegan, and free from preservatives. I']"
+                }
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+            "single": [
+                {
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+                }
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+                    "trace": "(line 715)  AssertionError: Items in the second set but not the first:"
+                },
+                {
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+                }
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+                },
+                {
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+                }
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+            "single": "https://github.com/huggingface/transformers/actions/runs/16712966867/job/47301330503"
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+                },
+                {
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+                },
+                {
+                    "line": "tests/models/t5/test_modeling_t5.py::T5ModelIntegrationTests::test_small_integration_test",
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+                },
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+                    "trace": "(line 727)  AssertionError: False is not true"
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+            "Tokenizers": {
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+            },
+            "Auto": {
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+                "single": 0,
+                "multi": 0
+            },
+            "Quantization": {
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+                "single": 0,
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+            },
+            "Unclassified": {
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+                "single": 0,
+                "multi": 0
+            }
+        },
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+        "success": 135,
+        "skipped": 93,
+        "time_spent": "9.85, 7.74, ",
+        "failures": {},
+        "job_link": {
+            "multi": "https://github.com/huggingface/transformers/actions/runs/16712966867/job/47301329875",
+            "single": "https://github.com/huggingface/transformers/actions/runs/16712966867/job/47301330596"
+        }
+    },
+    "models_wav2vec2": {
+        "failed": {
+            "PyTorch": {
+                "unclassified": 0,
+                "single": 0,
+                "multi": 0
+            },
+            "TensorFlow": {
+                "unclassified": 0,
+                "single": 0,
+                "multi": 0
+            },
+            "Flax": {
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+                "single": 0,
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+            "Tokenizers": {
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+            },
+            "Pipelines": {
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+                "single": 0,
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+            "ONNX": {
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+            "Auto": {
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+                "single": 0,
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+            "Quantization": {
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+                "multi": 0
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+                "single": 0,
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+            }
+        },
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+        "success": 292,
+        "skipped": 246,
+        "time_spent": "0:01:56, 0:01:54, ",
+        "failures": {},
+        "job_link": {
+            "multi": "https://github.com/huggingface/transformers/actions/runs/16712966867/job/47301329877",
+            "single": "https://github.com/huggingface/transformers/actions/runs/16712966867/job/47301330632"
+        }
+    },
+    "models_whisper": {
+        "failed": {
+            "PyTorch": {
+                "unclassified": 0,
+                "single": 40,
+                "multi": 42
+            },
+            "TensorFlow": {
+                "unclassified": 0,
+                "single": 0,
+                "multi": 0
+            },
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+                "single": 0,
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+            },
+            "Tokenizers": {
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+            },
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+                "single": 0,
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+            "Auto": {
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+                "single": 0,
+                "multi": 0
+            },
+            "Quantization": {
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+                "single": 0,
+                "multi": 0
+            },
+            "Unclassified": {
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+                "single": 0,
+                "multi": 0
+            }
+        },
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+        "success": 537,
+        "skipped": 337,
+        "time_spent": "0:03:23, 0:03:02, ",
+        "failures": {
+            "single": [
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_distil_token_timestamp_generation",
+                    "trace": "(line 2938)  Failed: (subprocess)"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_generate_with_forced_decoder_ids",
+                    "trace": "(line 2938)  Failed: (subprocess)"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_generate_with_prompt_ids",
+                    "trace": "(line 2938)  Failed: (subprocess)"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_generate_with_prompt_ids_task_language",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_language_detection",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_batched_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_batched_generation_multilingual",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_generation_multilingual",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_logits_librispeech",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_timestamp_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_small_en_logits_librispeech",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_small_longform_timestamps_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_small_token_timestamp_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_speculative_decoding_distil",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_speculative_decoding_non_distil",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_en_batched_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_en_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_logits_librispeech",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_longform_timestamps_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_specaugment_librispeech",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_static_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_static_generation_long_form",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_timestamp_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_token_timestamp_batch_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_token_timestamp_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_token_timestamp_generation_longform",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_empty_longform",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_multi_batch",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_multi_batch_hard",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_multi_batch_hard_prev_cond",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_multi_batch_prev_cond",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_no_speech_detection",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_prompt_ids",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_single_batch",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_single_batch_beam",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_single_batch_prev_cond",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_shortform_multi_batch_hard_prev_cond",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_shortform_single_batch_prev_cond",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                }
+            ],
+            "multi": [
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelTest::test_multi_gpu_data_parallel_forward",
+                    "trace": "(line 2938)  Failed: (subprocess)"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_distil_token_timestamp_generation",
+                    "trace": "(line 2938)  Failed: (subprocess)"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_generate_with_forced_decoder_ids",
+                    "trace": "(line 2938)  Failed: (subprocess)"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_generate_with_prompt_ids",
+                    "trace": "(line 131)  TypeError: EncoderDecoderCache.__init__() missing 1 required positional argument: 'cross_attention_cache'"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_generate_with_prompt_ids_task_language",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_language_detection",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_batched_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_batched_generation_multilingual",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_generation_multilingual",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_logits_librispeech",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_timestamp_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_small_en_logits_librispeech",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_small_longform_timestamps_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_small_token_timestamp_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_speculative_decoding_distil",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_speculative_decoding_non_distil",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_en_batched_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_en_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_logits_librispeech",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_longform_timestamps_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_specaugment_librispeech",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_static_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_static_generation_long_form",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_timestamp_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_token_timestamp_batch_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_token_timestamp_generation",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_token_timestamp_generation_longform",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_empty_longform",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_empty_longform_multi_gpu",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_multi_batch",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_multi_batch_hard",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_multi_batch_hard_prev_cond",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_multi_batch_prev_cond",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_no_speech_detection",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_prompt_ids",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_single_batch",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
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+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_single_batch_prev_cond",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_shortform_multi_batch_hard_prev_cond",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_shortform_single_batch_prev_cond",
+                    "trace": "(line 172)  ImportError: To support decoding audio data, please install 'torchcodec'."
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+            "multi": "https://github.com/huggingface/transformers/actions/runs/16712966867/job/47301329883"
+        }
+    }
+}

sample_nvidia.json

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+                }
+            ]
+        },
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+                    "trace": "(line 675)  AssertionError: Lists differ: ['sys[314 chars]ion\\n addCriterion\\n\\n addCriterion\\n\\n addCri[75 chars]n\\n'] != ['sys[314 chars]ion\\nThe dog in the picture appears to be a La[81 chars] is']"
+                }
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+            "single": "https://github.com/huggingface/transformers/actions/runs/16712955100/job/47301215911"
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+                    "trace": "(line 131)  TypeError: EncoderDecoderCache.__init__() missing 1 required positional argument: 'cross_attention_cache'"
+                },
+                {
+                    "line": "tests/models/t5/test_modeling_t5.py::T5ModelIntegrationTests::test_export_t5_summarization",
+                    "trace": "(line 687)  AttributeError: 'dict' object has no attribute 'batch_size'"
+                }
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+                    "line": "tests/models/t5/test_modeling_t5.py::T5ModelIntegrationTests::test_export_t5_summarization",
+                    "trace": "(line 687)  AttributeError: 'dict' object has no attribute 'batch_size'"
+                }
+            ]
+        },
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+            "single": "https://github.com/huggingface/transformers/actions/runs/16712955100/job/47301216464"
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+                "single": 4,
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+                "single": 0,
+                "multi": 0
+            },
+            "Unclassified": {
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+                "single": 0,
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+        "time_spent": "0:03:37, 0:03:36, ",
+        "failures": {
+            "multi": [
+                {
+                    "line": "tests/models/wav2vec2/test_modeling_wav2vec2.py::Wav2Vec2ModelIntegrationTest::test_inference_mms_1b_all",
+                    "trace": "(line 989)  RuntimeError: Dataset scripts are no longer supported, but found common_voice_11_0.py"
+                },
+                {
+                    "line": "tests/models/wav2vec2/test_modeling_wav2vec2.py::Wav2Vec2ModelIntegrationTest::test_wav2vec2_with_lm",
+                    "trace": "(line 989)  RuntimeError: Dataset scripts are no longer supported, but found common_voice_11_0.py"
+                },
+                {
+                    "line": "tests/models/wav2vec2/test_modeling_wav2vec2.py::Wav2Vec2ModelIntegrationTest::test_wav2vec2_with_lm_invalid_pool",
+                    "trace": "(line 675)  AssertionError: Traceback (most recent call last):"
+                },
+                {
+                    "line": "tests/models/wav2vec2/test_modeling_wav2vec2.py::Wav2Vec2ModelIntegrationTest::test_wav2vec2_with_lm_pool",
+                    "trace": "(line 989)  RuntimeError: Dataset scripts are no longer supported, but found common_voice_11_0.py"
+                }
+            ],
+            "single": [
+                {
+                    "line": "tests/models/wav2vec2/test_modeling_wav2vec2.py::Wav2Vec2ModelIntegrationTest::test_inference_mms_1b_all",
+                    "trace": "(line 989)  RuntimeError: Dataset scripts are no longer supported, but found common_voice_11_0.py"
+                },
+                {
+                    "line": "tests/models/wav2vec2/test_modeling_wav2vec2.py::Wav2Vec2ModelIntegrationTest::test_wav2vec2_with_lm",
+                    "trace": "(line 989)  RuntimeError: Dataset scripts are no longer supported, but found common_voice_11_0.py"
+                },
+                {
+                    "line": "tests/models/wav2vec2/test_modeling_wav2vec2.py::Wav2Vec2ModelIntegrationTest::test_wav2vec2_with_lm_invalid_pool",
+                    "trace": "(line 675)  AssertionError: Traceback (most recent call last):"
+                },
+                {
+                    "line": "tests/models/wav2vec2/test_modeling_wav2vec2.py::Wav2Vec2ModelIntegrationTest::test_wav2vec2_with_lm_pool",
+                    "trace": "(line 989)  RuntimeError: Dataset scripts are no longer supported, but found common_voice_11_0.py"
+                }
+            ]
+        },
+        "job_link": {
+            "multi": "https://github.com/huggingface/transformers/actions/runs/16712955100/job/47301216956",
+            "single": "https://github.com/huggingface/transformers/actions/runs/16712955100/job/47301216929"
+        }
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+                "single": 5,
+                "multi": 6
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+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_batched_generation_multilingual",
+                    "trace": "(line 756)  RuntimeError: The frame has 0 channels, expected 1. If you are hitting this, it may be because you are using a buggy FFmpeg version. FFmpeg4 is known to fail here in some valid scenarios. Try to upgrade FFmpeg?"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_small_longform_timestamps_generation",
+                    "trace": "(line 756)  RuntimeError: The frame has 0 channels, expected 1. If you are hitting this, it may be because you are using a buggy FFmpeg version. FFmpeg4 is known to fail here in some valid scenarios. Try to upgrade FFmpeg?"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_longform_timestamps_generation",
+                    "trace": "(line 756)  RuntimeError: The frame has 0 channels, expected 1. If you are hitting this, it may be because you are using a buggy FFmpeg version. FFmpeg4 is known to fail here in some valid scenarios. Try to upgrade FFmpeg?"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_multi_batch_hard",
+                    "trace": "(line 675)  AssertionError: Lists differ: [\" Fo[272 chars]ting of classics, Sicilian, nade door variatio[8147 chars]le!'] != [\" Fo[272 chars]ting a classic Sicilian, nade door variation o[8150 chars]le!']"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_shortform_single_batch_prev_cond",
+                    "trace": "(line 675)  AssertionError: Lists differ: [\" Fo[268 chars]ating, so soft, it would make JD power and her[196 chars]ke.\"] != [\" Fo[268 chars]ating so soft, it would make JD power and her [195 chars]ke.\"]"
+                }
+            ],
+            "multi": [
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelTest::test_multi_gpu_data_parallel_forward",
+                    "trace": "(line 131)  TypeError: EncoderDecoderCache.__init__() missing 1 required positional argument: 'cross_attention_cache'"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_large_batched_generation_multilingual",
+                    "trace": "(line 756)  RuntimeError: The frame has 0 channels, expected 1. If you are hitting this, it may be because you are using a buggy FFmpeg version. FFmpeg4 is known to fail here in some valid scenarios. Try to upgrade FFmpeg?"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_small_longform_timestamps_generation",
+                    "trace": "(line 756)  RuntimeError: The frame has 0 channels, expected 1. If you are hitting this, it may be because you are using a buggy FFmpeg version. FFmpeg4 is known to fail here in some valid scenarios. Try to upgrade FFmpeg?"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_tiny_longform_timestamps_generation",
+                    "trace": "(line 756)  RuntimeError: The frame has 0 channels, expected 1. If you are hitting this, it may be because you are using a buggy FFmpeg version. FFmpeg4 is known to fail here in some valid scenarios. Try to upgrade FFmpeg?"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_longform_multi_batch_hard",
+                    "trace": "(line 675)  AssertionError: Lists differ: [\" Fo[272 chars]ting of classics, Sicilian, nade door variatio[8147 chars]le!'] != [\" Fo[272 chars]ting a classic Sicilian, nade door variation o[8150 chars]le!']"
+                },
+                {
+                    "line": "tests/models/whisper/test_modeling_whisper.py::WhisperModelIntegrationTests::test_whisper_shortform_single_batch_prev_cond",
+                    "trace": "(line 675)  AssertionError: Lists differ: [\" Fo[268 chars]ating, so soft, it would make JD power and her[196 chars]ke.\"] != [\" Fo[268 chars]ating so soft, it would make JD power and her [195 chars]ke.\"]"
+                }
+            ]
+        },
+        "job_link": {
+            "single": "https://github.com/huggingface/transformers/actions/runs/16712955100/job/47301216943",
+            "multi": "https://github.com/huggingface/transformers/actions/runs/16712955100/job/47301217012"
+        }
+    }
+}

styles.css

@@ -0,0 +1,669 @@
+/* Global dark theme with configurable bottom margin */
+:root {
+    --main-content-bottom-margin: 10px; /* Configurable bottom margin for main content */
+}
+
+.gradio-container {
+    background-color: #000000 !important;
+    color: white !important;
+    height: 100vh !important;
+    max-height: 100vh !important;
+    overflow: hidden !important;
+}
+
+/* Remove borders from all components */
+.gr-box, .gr-form, .gr-panel {
+    border: none !important;
+    background-color: #000000 !important;
+}
+
+/* Simplified sidebar styling */
+.sidebar {
+    background: linear-gradient(145deg, #111111, #1a1a1a) !important;
+    border: none !important;
+    padding: 15px !important;
+    margin: 0 !important;
+    height: 100vh !important;
+    position: fixed !important;
+    left: 0 !important;
+    top: 0 !important;
+    width: 300px !important;
+    box-sizing: border-box !important;
+    overflow-y: auto !important;
+    overflow-x: hidden !important;
+}
+
+/* Target the actual Gradio column containing sidebar */
+div[data-testid="column"]:has(.sidebar) {
+    height: 100vh !important;
+    overflow-y: auto !important;
+    overflow-x: hidden !important;
+}
+
+/* Individual sidebar elements */
+.sidebar-title {
+    margin-bottom: 10px !important;
+}
+
+.sidebar-description {
+    margin-bottom: 15px !important;
+}
+
+/* Summary button styling - distinct from model buttons */
+.summary-button {
+    background: linear-gradient(135deg, #4a4a4a, #3e3e3e) !important;
+    color: white !important;
+    border: 2px solid #555555 !important;
+    margin: 0 0 15px 0 !important;
+    border-radius: 5px !important;
+    padding: 12px 10px !important;
+    transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1) !important;
+    position: relative !important;
+    overflow: hidden !important;
+    box-shadow:
+        0 4px 15px rgba(0, 0, 0, 0.3),
+        inset 0 1px 0 rgba(255, 255, 255, 0.2) !important;
+    font-weight: 600 !important;
+    font-size: 14px !important;
+    text-transform: uppercase !important;
+    letter-spacing: 0.3px !important;
+    font-family: monospace !important;
+    height: 60px !important;
+    display: flex !important;
+    flex-direction: column !important;
+    justify-content: center !important;
+    align-items: center !important;
+    line-height: 1.2 !important;
+    width: 100% !important;
+    max-width: 100% !important;
+    min-width: 0 !important;
+    box-sizing: border-box !important;
+}
+
+.model-header {
+    margin-bottom: 10px !important;
+    background: linear-gradient(135deg, #2a2a2a, #1e1e1e) !important;
+    color: white !important;
+    border: 1px solid #333 !important;
+    border-radius: 5px !important;
+    font-weight: 600 !important;
+    font-size: 14px !important;
+    font-family: monospace !important;
+    text-align: left !important;
+    width: 100% !important;
+}
+
+.model-header:hover {
+    background: linear-gradient(135deg, #3a3a3a, #2e2e2e) !important;
+}
+
+.sidebar-links {
+    margin-top: 15px !important;
+}
+
+/* Hide scrollbar for model container */
+.model-container::-webkit-scrollbar {
+    display: none !important;
+}
+
+/* Ensure all sidebar content fits within width */
+.sidebar * {
+    max-width: 100% !important;
+    word-wrap: break-word !important;
+    overflow-wrap: break-word !important;
+}
+
+/* Specific control for markdown content */
+.sidebar .markdown,
+.sidebar h1,
+.sidebar h2,
+.sidebar h3,
+.sidebar p {
+    max-width: 100% !important;
+    word-wrap: break-word !important;
+    overflow: hidden !important;
+}
+
+/* Sidebar scrollbar styling */
+.sidebar::-webkit-scrollbar {
+    width: 8px !important;
+    background: #111111 !important;
+}
+
+.sidebar::-webkit-scrollbar-track {
+    background: #111111 !important;
+}
+
+.sidebar::-webkit-scrollbar-thumb {
+    background-color: #333333 !important;
+    border-radius: 4px !important;
+}
+
+.sidebar::-webkit-scrollbar-thumb:hover {
+    background-color: #555555 !important;
+}
+
+/* Force button containers to single column in model list */
+.model-list .gr-button,
+.model-list button {
+    display: block !important;
+    width: 100% !important;
+    max-width: 100% !important;
+    margin: 4px 0 !important;
+    flex: none !important;
+}
+
+/* Simple unfolding menu with invisible scrollbar */
+.model-list-visible {
+    max-height: 200px !important;
+    overflow-y: auto !important;
+    transition: max-height 0.3s ease !important;
+    scrollbar-width: none !important;
+    -ms-overflow-style: none !important;
+}
+
+.model-list-visible::-webkit-scrollbar {
+    width: 0px !important;
+    background: transparent !important;
+}
+
+.model-list-hidden {
+    max-height: 0 !important;
+    overflow: hidden !important;
+    transition: max-height 0.3s ease !important;
+}
+
+
+/* Model button styling */
+.model-button {
+    background: linear-gradient(135deg, #2a2a2a, #1e1e1e) !important;
+    color: white !important;
+    margin: 3px 0 !important;
+    padding: 8px 12px !important;
+    font-weight: 600 !important;
+    font-size: 14px !important;
+    text-transform: uppercase !important;
+    letter-spacing: 0.3px !important;
+    font-family: monospace !important;
+    width: 100% !important;
+    max-width: 100% !important;
+    white-space: nowrap !important;
+    text-overflow: ellipsis !important;
+    display: block !important;
+    cursor: pointer !important;
+    transition: all 0.3s ease !important;
+    border: 1px solid #333 !important;
+    border-radius: 5px !important;
+}
+
+.model-button:hover {
+    background: linear-gradient(135deg, #3a3a3a, #2e2e2e) !important;
+    border-color: #74b9ff !important;
+    color: #74b9ff !important;
+    transform: translateY(-1px) !important;
+    box-shadow: 0 2px 8px rgba(116, 185, 255, 0.2) !important;
+}
+
+/* Model buttons with failures - fuzzy red border with inner glow */
+.model-button-failed {
+    border: 1px solid #712626 !important;
+    box-shadow: inset 0 0 8px rgba(204, 68, 68, 0.4) !important;
+}
+
+.model-button-failed:hover {
+    border-color: #712626 !important;
+    box-shadow: 0 0 12px rgba(255, 107, 107, 0.5) !important;
+}
+
+/*
+.model-button:active {
+    background: linear-gradient(135deg, #2a2a2a, #1e1e1e) !important;
+    color: #5a9bd4 !important;
+}
+*/
+
+/* Model stats badge */
+.model-stats {
+    display: flex !important;
+    justify-content: space-between !important;
+    align-items: center !important;
+    margin-top: 8px !important;
+    font-size: 12px !important;
+    opacity: 0.8 !important;
+}
+
+.stats-badge {
+    background: rgba(116, 185, 255, 0.2) !important;
+    padding: 4px 8px !important;
+    border-radius: 10px !important;
+    font-weight: 500 !important;
+    font-size: 11px !important;
+    color: #74b9ff !important;
+}
+
+.success-indicator {
+    width: 8px !important;
+    height: 8px !important;
+    border-radius: 50% !important;
+    display: inline-block !important;
+    margin-right: 6px !important;
+}
+
+.success-high { background-color: #4CAF50 !important; }
+.success-medium { background-color: #FF9800 !important; }
+.success-low { background-color: #F44336 !important; }
+
+/* Refresh button styling */
+.refresh-button {
+    background: linear-gradient(135deg, #2d5aa0, #1e3f73) !important;
+    color: white !important;
+    border: 1px solid #3a6bc7 !important;
+    margin: 0 0 10px 0 !important;
+    border-radius: 5px !important;
+    padding: 6px 8px !important;
+    transition: all 0.3s ease !important;
+    font-weight: 500 !important;
+    font-size: 11px !important;
+    text-transform: lowercase !important;
+    letter-spacing: 0.1px !important;
+    font-family: monospace !important;
+    width: 100% !important;
+    max-width: 100% !important;
+    min-width: 0 !important;
+    box-sizing: border-box !important;
+    white-space: nowrap !important;
+    overflow: hidden !important;
+    text-overflow: ellipsis !important;
+}
+
+.refresh-button:hover {
+    background: linear-gradient(135deg, #3a6bc7, #2d5aa0) !important;
+    border-color: #4a7bd9 !important;
+}
+
+/* Summary button styling - distinct from model buttons */
+.summary-button {
+    background: linear-gradient(135deg, #4a4a4a, #3e3e3e) !important;
+    color: white !important;
+    border: 2px solid #555555 !important;
+    margin: 0 0 15px 0 !important;
+    border-radius: 5px !important;
+    padding: 12px 10px !important;
+    transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1) !important;
+    position: relative !important;
+    overflow: hidden !important;
+    box-shadow:
+        0 4px 15px rgba(0, 0, 0, 0.3),
+        inset 0 1px 0 rgba(255, 255, 255, 0.2) !important;
+    font-weight: 600 !important;
+    font-size: 14px !important;
+    text-transform: uppercase !important;
+    letter-spacing: 0.3px !important;
+    font-family: monospace !important;
+    height: 60px !important;
+    display: flex !important;
+    flex-direction: column !important;
+    justify-content: center !important;
+    align-items: center !important;
+    line-height: 1.2 !important;
+    width: 100% !important;
+    max-width: 100% !important;
+    min-width: 0 !important;
+    box-sizing: border-box !important;
+}
+
+/* Simplified Gradio layout control */
+.sidebar .gr-column,
+.sidebar .gradio-column {
+    width: 100% !important;
+}
+
+/* Simplified Gradio targeting */
+div[data-testid="column"]:has(.sidebar) {
+    width: 300px !important;
+    min-width: 300px !important;
+}
+
+/* Button container with fixed height - DISABLED */
+/*
+.button-container {
+    height: 50vh !important;
+    max-height: 50vh !important;
+    overflow-y: auto !important;
+    overflow-x: hidden !important;
+    scrollbar-width: thin !important;
+    scrollbar-color: #333333 #111111 !important;
+    width: 100% !important;
+    max-width: 100% !important;
+    box-sizing: border-box !important;
+    padding: 5px 0 !important;
+    margin-top: 10px !important;
+}
+*/
+
+/* Removed simple scroll CSS - was hiding buttons */
+
+.summary-button:hover {
+    background: linear-gradient(135deg, #5a5a5a, #4e4e4e) !important;
+    color: #74b9ff !important;
+    border-color: #666666 !important;
+}
+
+.summary-button:active {
+    background: linear-gradient(135deg, #4a4a4a, #3e3e3e) !important;
+    color: #5a9bd4 !important;
+}
+
+/* Regular button styling for non-model buttons */
+.gr-button:not(.model-button):not(.summary-button) {
+    background-color: #222222 !important;
+    color: white !important;
+    border: 1px solid #444444 !important;
+    margin: 5px 0 !important;
+    border-radius: 8px !important;
+    transition: all 0.3s ease !important;
+}
+
+.gr-button:not(.model-button):not(.summary-button):hover {
+    background-color: #333333 !important;
+    border-color: #666666 !important;
+}
+
+/* Plot container with smooth transitions and controlled scrolling */
+.plot-container {
+    background-color: #000000 !important;
+    border: none !important;
+    transition: opacity 0.6s ease-in-out !important;
+    flex: 1 1 auto !important;
+    min-height: 0 !important;
+    overflow-y: auto !important;
+    scrollbar-width: thin !important;
+    scrollbar-color: #333333 #000000 !important;
+}
+
+/* Custom scrollbar for plot container */
+.plot-container::-webkit-scrollbar {
+    width: 8px !important;
+    background: #000000 !important;
+}
+
+.plot-container::-webkit-scrollbar-track {
+    background: #000000 !important;
+}
+
+.plot-container::-webkit-scrollbar-thumb {
+    background-color: #333333 !important;
+    border-radius: 4px !important;
+}
+
+.plot-container::-webkit-scrollbar-thumb:hover {
+    background-color: #555555 !important;
+}
+
+/* Gradio plot component styling */
+.gr-plot {
+    background-color: #000000 !important;
+    transition: opacity 0.6s ease-in-out !important;
+}
+
+.gr-plot .gradio-plot {
+    background-color: #000000 !important;
+    transition: opacity 0.6s ease-in-out !important;
+}
+
+.gr-plot img {
+    transition: opacity 0.6s ease-in-out !important;
+}
+
+/* Target the plot wrapper */
+div[data-testid="plot"] {
+    background-color: #000000 !important;
+}
+
+/* Target all possible plot containers */
+.plot-container img,
+.gr-plot img,
+.gradio-plot img {
+    background-color: #000000 !important;
+}
+
+/* Ensure plot area background */
+.gr-plot > div,
+.plot-container > div {
+    background-color: #000000 !important;
+}
+
+/* Prevent white flash during plot updates */
+.plot-container::before {
+    content: "";
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background-color: #000000;
+    z-index: -1;
+}
+
+/* Force all plot elements to have black background */
+.plot-container *,
+.gr-plot *,
+div[data-testid="plot"] * {
+    background-color: #000000 !important;
+}
+
+/* Override any white backgrounds in matplotlib */
+.plot-container canvas,
+.gr-plot canvas {
+    background-color: #000000 !important;
+}
+
+/* Text elements */
+h1, h2, h3, p, .markdown {
+    color: white !important;
+}
+
+/* Sidebar header enhancement */
+.sidebar h1 {
+    background: linear-gradient(45deg, #74b9ff, #a29bfe) !important;
+    -webkit-background-clip: text !important;
+    -webkit-text-fill-color: transparent !important;
+    background-clip: text !important;
+    text-align: center !important;
+    margin-bottom: 15px !important;
+    font-size: 28px !important;
+    font-weight: 700 !important;
+    font-family: monospace !important;
+}
+
+/* Sidebar description text */
+.sidebar p {
+    text-align: center !important;
+    margin-bottom: 20px !important;
+    line-height: 1.5 !important;
+    font-size: 14px !important;
+    font-family: monospace !important;
+}
+
+/* CI Links styling */
+.sidebar a {
+    color: #74b9ff !important;
+    text-decoration: none !important;
+    font-weight: 500 !important;
+    font-family: monospace !important;
+    transition: color 0.3s ease !important;
+}
+
+.sidebar a:hover {
+    color: #a29bfe !important;
+    text-decoration: underline !important;
+}
+
+.sidebar strong {
+    color: #74b9ff !important;
+    font-weight: 600 !important;
+    font-family: monospace !important;
+}
+
+.sidebar em {
+    color: #a29bfe !important;
+    font-style: normal !important;
+    opacity: 0.9 !important;
+    font-family: monospace !important;
+}
+
+/* Remove all borders globally */
+* {
+    border-color: transparent !important;
+}
+
+/* Main content area */
+.main-content {
+    background-color: #000000 !important;
+    padding: 0px 20px var(--main-content-bottom-margin, 10px) 20px !important;
+    margin-left: 300px !important;
+    height: 100vh !important;
+    overflow-y: auto !important;
+    box-sizing: border-box !important;
+    display: flex !important;
+    flex-direction: column !important;
+}
+
+/* Custom scrollbar for main content */
+.main-content {
+    scrollbar-width: thin !important;
+    scrollbar-color: #333333 #000000 !important;
+}
+
+.main-content::-webkit-scrollbar {
+    width: 8px !important;
+    background: #000000 !important;
+}
+
+.main-content::-webkit-scrollbar-track {
+    background: #000000 !important;
+}
+
+.main-content::-webkit-scrollbar-thumb {
+    background-color: #333333 !important;
+    border-radius: 4px !important;
+}
+
+.main-content::-webkit-scrollbar-thumb:hover {
+    background-color: #555555 !important;
+}
+
+/* Failed tests display - seamless appearance with constrained height */
+.failed-tests textarea {
+    background-color: #000000 !important;
+    color: #FFFFFF !important;
+    font-family: monospace !important;
+    font-size: 14px !important;
+    border: none !important;
+    padding: 10px !important;
+    outline: none !important;
+    line-height: 1.4 !important;
+    height: 180px !important;
+    max-height: 180px !important;
+    min-height: 180px !important;
+    overflow-y: auto !important;
+    resize: none !important;
+    scrollbar-width: thin !important;
+    scrollbar-color: #333333 #000000 !important;
+    scroll-behavior: auto !important;
+    transition: opacity 0.5s ease-in-out !important;
+    scroll-padding-top: 0 !important;
+}
+
+/* WebKit scrollbar styling for failed tests */
+.failed-tests textarea::-webkit-scrollbar {
+    width: 8px !important;
+}
+
+.failed-tests textarea::-webkit-scrollbar-track {
+    background: #000000 !important;
+}
+
+.failed-tests textarea::-webkit-scrollbar-thumb {
+    background-color: #333333 !important;
+    border-radius: 4px !important;
+}
+
+.failed-tests textarea::-webkit-scrollbar-thumb:hover {
+    background-color: #555555 !important;
+}
+
+/* Prevent white flash in text boxes during updates */
+.failed-tests::before {
+    content: "";
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background-color: #000000;
+    z-index: -1;
+}
+
+.failed-tests {
+    background-color: #000000 !important;
+    height: 200px !important;
+    max-height: 200px !important;
+    min-height: 200px !important;
+    position: relative;
+    transition: opacity 0.5s ease-in-out !important;
+    flex-shrink: 0 !important;
+}
+
+.failed-tests .gr-textbox {
+    background-color: #000000 !important;
+    border: none !important;
+    height: 180px !important;
+    max-height: 180px !important;
+    min-height: 180px !important;
+    transition: opacity 0.5s ease-in-out !important;
+}
+
+/* Force all textbox elements to have black background */
+.failed-tests *,
+.failed-tests .gr-textbox *,
+.failed-tests textarea * {
+    background-color: #000000 !important;
+}
+
+/* Summary display styling */
+.summary-display textarea {
+    background-color: #000000 !important;
+    color: #FFFFFF !important;
+    font-family: monospace !important;
+    font-size: 24px !important;
+    border: none !important;
+    padding: 20px !important;
+    outline: none !important;
+    line-height: 2 !important;
+    text-align: right !important;
+    resize: none !important;
+}
+
+.summary-display {
+    background-color: #000000 !important;
+}
+
+/* Detail view layout */
+.detail-view {
+    display: flex !important;
+    flex-direction: column !important;
+    height: 100% !important;
+    min-height: 0 !important;
+}
+
+/* JavaScript to reset scroll position */
+.scroll-reset {
+    animation: resetScroll 0.1s ease;
+}
+
+@keyframes resetScroll {
+    0% { scroll-behavior: auto; }
+    100% { scroll-behavior: auto; }
+}
+

summary_page.py

@@ -0,0 +1,208 @@
+import pandas as pd
+from data import extract_model_data
+import matplotlib.pyplot as plt
+
+# Layout parameters
+COLUMNS = 3
+
+# Derived constants
+COLUMN_WIDTH = 100 / COLUMNS  # Each column takes 25% of width
+BAR_WIDTH = COLUMN_WIDTH * 0.8  # 80% of column width for bars
+BAR_MARGIN = COLUMN_WIDTH * 0.1  # 10% margin on each side
+
+# Figure dimensions
+FIGURE_WIDTH = 22  # Wider to accommodate columns and legend
+MAX_HEIGHT = 14  # Maximum height in inches
+MIN_HEIGHT_PER_ROW = 2.8
+FIGURE_PADDING = 1
+
+# Bar styling
+BAR_HEIGHT_RATIO = 0.22  # Bar height as ratio of vertical spacing
+VERTICAL_SPACING_RATIO = 0.2  # Base vertical position ratio
+AMD_BAR_OFFSET = 0.25  # AMD bar offset ratio
+NVIDIA_BAR_OFFSET = 0.54  # NVIDIA bar offset ratio
+
+# Colors
+COLORS = {
+    'passed': '#4CAF50',
+    'failed': '#E53E3E',
+    'skipped': '#FFD54F',
+    'error': '#8B0000',
+    'empty': "#5B5B5B"
+}
+
+# Font styling
+MODEL_NAME_FONT_SIZE = 16
+LABEL_FONT_SIZE = 14
+LABEL_OFFSET = 1  # Distance of label from bar
+FAILURE_RATE_FONT_SIZE = 28
+
+
+def draw_text_and_bar(
+    label: str,
+    stats: dict[str, int],
+    y_bar: float,
+    column_left_position: float,
+    bar_height: float,
+    ax,
+) -> None:
+    """Draw a horizontal bar chart for given stats and its label on the left."""
+    # Text
+    label_x = column_left_position - LABEL_OFFSET
+    failures_present = any(stats[category] > 0 for category in ['failed', 'error'])
+    if failures_present:
+        props = dict(boxstyle='round', facecolor=COLORS['failed'], alpha=0.35)
+    else:
+        props = dict(alpha=0)
+    ax.text(
+        label_x, y_bar, label, ha='right', va='center', color='#CCCCCC', fontsize=LABEL_FONT_SIZE,
+        fontfamily='monospace', fontweight='normal', bbox=props
+    )
+    # Bar
+    total = sum(stats.values())
+    if total > 0:
+        left = column_left_position
+        for category in ['passed', 'failed', 'skipped', 'error']:
+            if stats[category] > 0:
+                width = stats[category] / total * BAR_WIDTH
+                ax.barh(y_bar, width, left=left, height=bar_height, color=COLORS[category], alpha=0.9)
+                left += width
+    else:
+        ax.barh(y_bar, BAR_WIDTH, left=column_left_position, height=bar_height, color=COLORS['empty'], alpha=0.9)
+
+def create_summary_page(df: pd.DataFrame, available_models: list[str]):
+    """Create a summary page with model names and both AMD/NVIDIA test stats bars."""
+    return None
+
+    # Calculate overall failure rates
+    amd_counts, nvidia_counts = get_overall_stats(df, available_models)
+
+    amd_failure_rate = (amd_counts[1] / sum(amd_counts)) if sum(amd_counts) > 0 else 0.0
+    amd_failure_rate *= 100
+    nvidia_failure_rate = (nvidia_counts[1] / sum(nvidia_counts)) if sum(nvidia_counts) > 0 else 0.0
+    nvidia_failure_rate *= 100
+
+    # Calculate dimensions for N-column layout
+    model_count = len(available_models)
+    rows = (model_count + COLUMNS - 1) // COLUMNS  # Ceiling division
+
+    # Figure dimensions - wider for columns, height based on rows
+    height_per_row = min(MIN_HEIGHT_PER_ROW, MAX_HEIGHT / max(rows, 1))
+    figure_height = min(MAX_HEIGHT, rows * height_per_row + FIGURE_PADDING)
+
+    fig = plt.figure(figsize=(FIGURE_WIDTH, figure_height), facecolor='#000000')
+    ax = fig.add_subplot(111)
+    ax.set_facecolor('#000000')
+    
+    # Add overall failure rates at the top as a proper title
+    failure_text = f"Overall Failure Rates: AMD {amd_failure_rate:.1f}%  |  NVIDIA {nvidia_failure_rate:.1f}%"
+    ax.text(50, -1.25, failure_text, ha='center', va='top', 
+           color='#FFFFFF', fontsize=FAILURE_RATE_FONT_SIZE,
+           fontfamily='monospace', fontweight='bold')
+
+    visible_model_count = 0
+    max_y = 0
+
+    for i, model_name in enumerate(available_models):
+        if model_name not in df.index:
+            continue
+
+        row = df.loc[model_name]
+
+        # Extract and process model data
+        amd_stats, nvidia_stats = extract_model_data(row)[:2]
+
+        # Calculate position in 4-column grid
+        col = visible_model_count % COLUMNS
+        row = visible_model_count // COLUMNS
+
+        # Calculate horizontal position for this column
+        col_left = col * COLUMN_WIDTH + BAR_MARGIN
+        col_center = col * COLUMN_WIDTH + COLUMN_WIDTH / 2
+
+        # Calculate vertical position for this row - start from top
+        vertical_spacing = height_per_row
+        y_base = (VERTICAL_SPACING_RATIO + row) * vertical_spacing
+        y_model_name = y_base    # Model name above AMD bar
+        y_amd_bar = y_base + vertical_spacing * AMD_BAR_OFFSET       # AMD bar
+        y_nvidia_bar = y_base + vertical_spacing * NVIDIA_BAR_OFFSET    # NVIDIA bar
+        max_y = max(max_y, y_nvidia_bar + vertical_spacing * 0.3)
+
+        # Model name centered above the bars in this column
+        ax.text(col_center, y_model_name, model_name.lower(),
+               ha='center', va='center', color='#FFFFFF',
+               fontsize=MODEL_NAME_FONT_SIZE, fontfamily='monospace', fontweight='bold')
+
+        # AMD label and bar in this column
+        bar_height = min(0.4, vertical_spacing * BAR_HEIGHT_RATIO)
+        # Draw AMD bar
+        draw_text_and_bar("amd", amd_stats, y_amd_bar, col_left, bar_height, ax)
+        # Draw NVIDIA bar
+        draw_text_and_bar("nvidia", nvidia_stats, y_nvidia_bar, col_left, bar_height, ax)
+
+        # Increment counter for next visible model
+        visible_model_count += 1
+
+
+    # Add AMD and NVIDIA test totals in the bottom left
+    # Calculate line spacing to align middle with legend
+    line_height = 0.4  # Height between lines
+    legend_y = max_y + 1
+
+    # Position the two lines so their middle aligns with legend_y
+    amd_y = legend_y - line_height / 2
+    nvidia_y = legend_y + line_height / 2
+
+    amd_totals_text =    f"AMD Tests    - Passed: {amd_counts[0]}, Failed: {amd_counts[1]}, Skipped: {amd_counts[2]}"
+    nvidia_totals_text = f"NVIDIA Tests - Passed: {nvidia_counts[0]}, Failed: {nvidia_counts[1]}, Skipped: {nvidia_counts[2]}"
+
+    ax.text(0, amd_y, amd_totals_text,
+           ha='left', va='bottom', color='#CCCCCC',
+           fontsize=14, fontfamily='monospace')
+
+    ax.text(0, nvidia_y, nvidia_totals_text,
+           ha='left', va='bottom', color='#CCCCCC',
+           fontsize=14, fontfamily='monospace')
+
+    # Add legend horizontally in bottom right corner
+    patch_height = 0.3
+    patch_width = 3
+
+    legend_start_x = 68.7
+    legend_y = max_y + 1
+    legend_spacing = 10
+    legend_font_size = 15
+
+    # Legend entries
+    legend_items = [
+        ('passed', 'Passed'),
+        ('failed', 'Failed'), 
+        ('skipped', 'Skipped'),
+    ]
+    
+    for i, (status, label) in enumerate(legend_items):
+        x_pos = legend_start_x + i * legend_spacing
+        # Small colored square
+        ax.add_patch(plt.Rectangle((x_pos - 0.6, legend_y), patch_width, -patch_height,
+                                 facecolor=COLORS[status], alpha=0.9))
+        # Status label
+        ax.text(x_pos + patch_width, legend_y, label, 
+               ha='left', va='bottom', color='#CCCCCC', 
+               fontsize=legend_font_size, fontfamily='monospace')
+
+    # Style the axes to be completely invisible and span full width
+    ax.set_xlim(-5, 105)  # Slightly wider to accommodate labels
+    ax.set_ylim(0, max_y + 1)  # Add some padding at the top for title
+    ax.set_xlabel('')
+    ax.set_ylabel('')
+    ax.spines['bottom'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.set_xticks([])
+    ax.set_yticks([])
+    ax.yaxis.set_inverted(True)
+
+    # Remove all margins to make figure stick to top
+    plt.tight_layout()
+    return fig

theme_config.py

@@ -1,167 +0,0 @@
-import gradio as gr
-import matplotlib.pyplot as plt
-import matplotlib as mpl
-
-class DashboardTheme:
-    # Color palette - Grey, minimalistic, sleek
-    PRIMARY_GREY = "#2C3E50"      # Dark slate grey
-    SECONDARY_GREY = "#34495E"    # Medium slate grey
-    LIGHT_GREY = "#7F8C8D"       # Light grey
-    BACKGROUND_GREY = "#ECF0F1"   # Very light grey background
-    WHITE = "#FFFFFF"
-    ACCENT_BLUE = "#3498DB"       # Clean blue accent
-    SUCCESS_GREEN = "#27AE60"     # Clean green
-    WARNING_ORANGE = "#F39C12"    # Clean orange
-    ERROR_RED = "#E74C3C"         # Clean red
-
-    # Chart colors - Professional grey scale with subtle accents
-    CHART_COLORS = [
-        "#34495E",  # Dark grey
-        "#5D6D7E",  # Medium grey
-        "#85929E",  # Light grey
-        "#3498DB",  # Accent blue
-        "#27AE60",  # Accent green
-        "#F39C12",  # Accent orange
-        "#9B59B6",  # Accent purple
-        "#E74C3C"   # Accent red
-    ]
-
-    # Typography
-    FONT_FAMILY = "'Inter', 'SF Pro Display', 'Helvetica Neue', Arial, sans-serif"
-
-    @staticmethod
-    def get_gradio_theme():
-        """Create a custom Gradio theme with grey minimalistic design"""
-        theme = gr.themes.Soft(
-            primary_hue=gr.themes.colors.slate,
-            secondary_hue=gr.themes.colors.gray,
-            neutral_hue=gr.themes.colors.slate,
-            font=gr.themes.GoogleFont("Inter")
-        ).set(
-            # Overall styling
-            body_background_fill=DashboardTheme.BACKGROUND_GREY,
-            background_fill_primary=DashboardTheme.WHITE,
-            background_fill_secondary=DashboardTheme.BACKGROUND_GREY,
-
-            # Border colors
-            border_color_primary=DashboardTheme.LIGHT_GREY,
-            border_color_accent=DashboardTheme.ACCENT_BLUE,
-
-            # Button styling
-            button_primary_background_fill=DashboardTheme.PRIMARY_GREY,
-            button_primary_background_fill_hover=DashboardTheme.SECONDARY_GREY,
-            button_primary_text_color=DashboardTheme.WHITE,
-            button_secondary_background_fill=DashboardTheme.WHITE,
-            button_secondary_border_color=DashboardTheme.LIGHT_GREY,
-            button_secondary_text_color=DashboardTheme.PRIMARY_GREY,
-
-            # Input styling
-            input_background_fill=DashboardTheme.WHITE,
-            input_border_color=DashboardTheme.LIGHT_GREY,
-
-            # Text colors
-            body_text_color=DashboardTheme.PRIMARY_GREY,
-            body_text_color_subdued=DashboardTheme.LIGHT_GREY,
-
-            # Panel styling
-            panel_background_fill=DashboardTheme.WHITE,
-            panel_border_color=DashboardTheme.LIGHT_GREY,
-
-            # Checkbox/Radio styling
-            checkbox_background_color=DashboardTheme.WHITE,
-            checkbox_background_color_selected=DashboardTheme.ACCENT_BLUE,
-            checkbox_border_color=DashboardTheme.LIGHT_GREY,
-
-            # Tab styling
-            block_title_text_color=DashboardTheme.PRIMARY_GREY,
-            block_label_text_color=DashboardTheme.SECONDARY_GREY,
-        )
-
-        return theme
-
-    @staticmethod
-    def setup_matplotlib_style():
-        """Configure matplotlib with the dashboard theme"""
-        # Set the style parameters
-        plt.style.use('default')  # Reset to default first
-
-        # Configure matplotlib parameters
-        mpl.rcParams.update({
-            'figure.facecolor': DashboardTheme.WHITE,
-            'axes.facecolor': DashboardTheme.WHITE,
-            'axes.edgecolor': DashboardTheme.LIGHT_GREY,
-            'axes.labelcolor': DashboardTheme.PRIMARY_GREY,
-            'axes.axisbelow': True,
-            'axes.grid': True,
-            'axes.spines.left': True,
-            'axes.spines.bottom': True,
-            'axes.spines.top': False,
-            'axes.spines.right': False,
-            'axes.linewidth': 1.0,
-
-            # Grid styling
-            'grid.color': DashboardTheme.LIGHT_GREY,
-            'grid.linestyle': '-',
-            'grid.linewidth': 0.5,
-            'grid.alpha': 0.3,
-
-            # Text styling
-            'text.color': DashboardTheme.PRIMARY_GREY,
-            'font.family': 'sans-serif',
-            'font.size': 10,
-            'axes.titlesize': 12,
-            'axes.labelsize': 10,
-            'xtick.labelsize': 9,
-            'ytick.labelsize': 9,
-            'legend.fontsize': 9,
-            'axes.titleweight': 'bold',
-
-            # Tick styling
-            'xtick.color': DashboardTheme.LIGHT_GREY,
-            'ytick.color': DashboardTheme.LIGHT_GREY,
-            'xtick.direction': 'out',
-            'ytick.direction': 'out',
-
-            # Legend styling
-            'legend.frameon': True,
-            'legend.facecolor': DashboardTheme.WHITE,
-            'legend.edgecolor': DashboardTheme.LIGHT_GREY,
-            'legend.shadow': False,
-            'legend.framealpha': 0.9,
-        })
-
-    @staticmethod
-    def get_chart_colors():
-        """Get the standardized chart color palette"""
-        return DashboardTheme.CHART_COLORS.copy()
-
-    @staticmethod
-    def get_status_colors():
-        """Get colors for different status indicators"""
-        return {
-            'success': DashboardTheme.SUCCESS_GREEN,
-            'warning': DashboardTheme.WARNING_ORANGE,
-            'error': DashboardTheme.ERROR_RED,
-            'info': DashboardTheme.ACCENT_BLUE,
-            'neutral': DashboardTheme.LIGHT_GREY
-        }
-
-# Common styling utilities
-def get_section_title_style():
-    """Get consistent styling for section titles"""
-    return {
-        'color': DashboardTheme.PRIMARY_GREY,
-        'font-weight': 'bold',
-        'font-size': '1.1em',
-        'margin-bottom': '0.5em'
-    }
-
-def get_metric_card_style():
-    """Get consistent styling for metric cards"""
-    return {
-        'background': DashboardTheme.WHITE,
-        'border': f'1px solid {DashboardTheme.LIGHT_GREY}',
-        'border-radius': '8px',
-        'padding': '1em',
-        'box-shadow': '0 2px 4px rgba(0,0,0,0.05)'
-    }

utils.py

@@ -0,0 +1,51 @@
+import logging
+import sys
+from datetime import datetime
+
+
+class TimestampFormatter(logging.Formatter):
+    """Custom formatter that matches the existing timestamp format used in print statements."""
+
+    def format(self, record):
+        # Create timestamp in the same format as existing print statements
+        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+        # Format the message with timestamp prefix
+        if record.levelno == logging.WARNING:
+            return f"WARNING: {record.getMessage()}"
+        elif record.levelno == logging.ERROR:
+            return f"Error {record.getMessage()}"
+        else:
+            return f"[{timestamp}] {record.getMessage()}"
+
+
+def setup_logger(name="tcid", level=logging.INFO):
+    """Set up logger with custom timestamp formatting to match existing print format."""
+    logger = logging.getLogger(name)
+
+    # Avoid adding multiple handlers if logger already exists
+    if logger.handlers:
+        return logger
+
+    logger.setLevel(level)
+
+    # Create console handler
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setLevel(level)
+
+    # Set custom formatter
+    formatter = TimestampFormatter()
+    handler.setFormatter(formatter)
+
+    logger.addHandler(handler)
+
+    return logger
+
+
+# Create default logger instance
+logger = setup_logger()
+
+
+
+def generate_underlined_line(text: str) -> str:
+    return text + "\n" + "─" * len(text)