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app.py

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+import gradio as gr
+import pandas as pd
+
+LEADERBOARD_PATH = "leaderboard.csv"
+
+
+def load_leaderboard():
+    try:
+        return pd.read_csv(LEADERBOARD_PATH)
+    except FileNotFoundError:
+        return pd.DataFrame([{"Status": "No leaderboard data available"}])
+
+
+def style_leaderboard(df: pd.DataFrame):
+    """Return styled HTML with highlighted best performers and professional formatting."""
+    if df.empty:
+        return "<p>No data available.</p>"
+
+    num_cols = [c for c in df.columns if c not in ["Rank", "Model"]]
+
+    def highlight_best(s):
+        if pd.api.types.is_numeric_dtype(s):
+            max_val = s.max()
+            return ['color: #6AA84F; font-weight: 600;' if v == max_val else '' for v in s]
+        return ['' for _ in s]
+    df = df.reset_index(drop=True)
+    styled = (df.style.apply(highlight_best, subset=num_cols, axis=0).format(precision=1).hide(axis='index'))
+
+    # Professional table styling
+    html = styled.to_html()
+    return f"""
+    <div style="margin: 20px 0;">
+        <div style="overflow-x: auto; border: 1px solid #e2e8f0; border-radius: 8px; box-shadow: 0 1px 3px rgba(0,0,0,0.1);">
+            <style>
+                table {{
+                    width: 100%;
+                    border-collapse: collapse;
+                    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
+                }}
+                th {{
+                    font-weight: 600;
+                    padding: 16px 12px;
+                    text-align: left;
+                    border-bottom: 2px solid #e2e8f0;
+                    font-size: 14px;
+                }}
+                td {{
+                    padding: 12px;
+                    border-bottom: 1px solid #f1f5f9;
+                    font-size: 14px;
+                }}
+                tr:hover {{
+                    background-color: #7c7d7e;
+                }}
+            </style>
+            {html}
+        </div>
+    </div>
+    """
+
+
+def leaderboard_view():
+    df = load_leaderboard()
+    return style_leaderboard(df)
+
+
+# ---------------- Gradio UI ---------------- #
+
+with gr.Blocks(css="""
+    .gradio-container {
+        max-width: 1200px !important; 
+        margin: auto;
+        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
+    }
+    #title-image {
+        margin: 20px auto; 
+        display: block; 
+        max-width: 800px;
+    }
+    .gr-markdown h1 {
+        color: #1e293b;
+        font-weight: 700;
+        margin-bottom: 16px;
+    }
+    .gr-markdown h2 {
+        color: #334155;
+        font-weight: 600;
+        margin-top: 24px;
+        margin-bottom: 12px;
+    }
+    .gr-markdown h3 {
+        color: #475569;
+        font-weight: 600;
+        margin-bottom: 8px;
+    }
+    .gr-markdown p {
+        color: #64748b;
+        line-height: 1.6;
+    }
+    .gr-tab-nav button {
+        font-weight: 500;
+    }
+""") as demo:
+    # Banner image
+    gr.Image("title.png", elem_id="title-image", show_label=False)
+
+    # Professional introduction
+    gr.Markdown("""
+# DFBench: The Image Deepfake Detection Benchmark 2025
+
+DFBench provides a standardized evaluation framework for computer vision deepfake detection systems.
+This leaderboard focuses on image deepfake detection, e.g. the output of text-to-image and image-to-image models.
+
+**Objectives:**
+- Allow fair comparison between deepfake detection models on unseen test data 
+- Advance the state-of-the-art in synthetic media identification
+
+This benchmark serves the academic and industry research community by providing consistent evaluation standards for deepfake detection methodologies.
+""")
+
+    with gr.Tab("Leaderboard"):
+        gr.Markdown("## Current Performance Rankings")
+        gr.HTML(leaderboard_view())
+        gr.Markdown("""
+*Leaderboard is updated upon validation of new submissions. All results are evaluated using standardized metrics on the official test dataset.*
+        """)
+
+    with gr.Tab("Submission Guidelines"):
+        gr.Markdown("""
+# Model Submission Process
+
+**Official Dataset:** [DFBench / Image-Deepfake-Detection-25](https://huggingface.co/datasets/DFBench/Image-Deepfake-Detection-25)
+
+The evaluation dataset comprises **2,920 images** with binary classification labels:
+- **Real:** Authentic, unmodified images
+- **Fake:** AI-generated or synthetically modified content
+
+---
+
+## Submission Requirements
+
+### File Format
+Submit predictions as a CSV file with the following structure: `filename,label`.
+- `filename`: Exact filename as provided in the dataset
+- `label`: Binary classification result (`real` or `fake`)
+
+ For example:
+
+```
+filename,label
+1.jpg,fake
+2.jpeg,real
+3.webp,fake
+...
+2920.png,fake
+```
+### Submission Process
+
+1. **Prediction Generation**: Generate predictions for all 2,920 test images
+2. **File Preparation**: Format results according to specification above
+3. **Submission**: Send your CSV file submission to: **submission@dfbench.com**. The name of the file should correspond to the leaderboard model name, e.g. `Model_This_name.csv` will be included as `Model This name` in the leaderboard.
+
+### Evaluation Timeline
+- Submissions are processed within 5-7 business days
+- Results are validated against ground truth labels
+- Approved submissions are added to the public leaderboard
+
+---
+
+## Technical Notes
+- Model names will be formatted for display (underscores converted to spaces)
+- Each research group may submit one set of scores per month
+- All submissions undergo automated validation before leaderboard inclusion
+
+## Legal Notes
+- The authors reserve the right to not publish or to remove a submission at their discretion  
+- Submissions may be excluded if found to violate ethical guidelines, contain malicious content, or appear fraudulent  
+- Benchmark maintainers may adjust evaluation protocols as the dataset and task evolve  
+- No warranties are provided regarding benchmark results, which are intended strictly for research and comparison purposes  
+
+
+For technical inquiries regarding the evaluation process, please contact the benchmark maintainers through the submission email.
+""")
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)