Update app.py

app.py

@@ -1,9 +1,98 @@
+import gradio as gr
+import numpy as np
 import torch
+import pandas as pd
+from chronos import ChronosPipeline
+from io import StringIO
 
-MODEL_PATH = "model/model.pt"
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# --- Model Loading ---
+# This part is outside the function so it only runs once when the app starts
+try:
+    model_name = "amazon/chronos-t5-small"
+    pipeline = ChronosPipeline.from_pretrained(
+        model_name,
+        device_map="cpu", # Force CPU usage for free tier
+        torch_dtype=torch.float32,
+    )
+    print(f"Loaded model: {model_name}")
+except Exception as e:
+    # A fallback in case the model fails to load
+    print(f"Error loading model: {e}")
+    pipeline = None
 
-print(f"Loading model from: {MODEL_PATH}")
-model = torch.load(MODEL_PATH, map_location=device, weights_only=False)
-model.to(device)
-model.eval()
+# --- Prediction Function ---
+def forecast_time_series(csv_file, prediction_length):
+    """
+    Takes a CSV file, extracts the last column (time series), and forecasts.
+    """
+    if pipeline is None:
+        return "Model failed to load. Please check logs/dependencies."
+
+    try:
+        # Read the CSV file content from the Gradio InputFile
+        content = csv_file.read().decode('utf-8')
+        df = pd.read_csv(StringIO(content))
+        
+        # Assume the time series data is in the last column
+        # and has no missing values
+        historical_data = df.iloc[:, -1].values
+        
+        if len(historical_data) < 50:
+             return "Please upload a time series with at least 50 historical points for a good forecast."
+
+        # Convert historical data to the required format
+        historical_series = torch.tensor(historical_data, dtype=torch.float32)
+
+        # Generate the forecast
+        forecast_samples = pipeline.predict(
+            historical_series,
+            prediction_length=int(prediction_length),
+            num_samples=20, # Number of probabilistic paths to generate
+        )
+        
+        # Calculate the median for the central prediction line
+        median_forecast = np.quantile(forecast_samples.numpy(), 0.5, axis=0)
+        
+        # Prepare the output data for plotting
+        historical_index = np.arange(len(historical_data))
+        forecast_index = np.arange(len(historical_data), len(historical_data) + int(prediction_length))
+        
+        # Create a single plot with both historical and forecast data
+        plot_data = {
+            "Historical": list(historical_data),
+            "Forecast": list(median_forecast),
+        }
+        
+        return {
+            "Historical": (historical_index, historical_data),
+            "Forecast": (forecast_index, median_forecast)
+        }
+
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+# --- Gradio Interface Setup ---
+# Define the example input file structure (for user convenience)
+example_data = [
+    [
+        'date,value\n2025-01-01,10.0\n2025-01-02,11.5\n...\n2025-03-20,15.2',
+        7
+    ] # A sample input isn't a file, so it can't be added directly here. 
+      # Users will need to upload a CSV file manually.
+]
+
+
+gr_plot = gr.Plot(label="Time Series Forecast (Historical + Predicted Median)")
+
+gr.Interface(
+    fn=forecast_time_series,
+    inputs=[
+        gr.File(label="Upload a CSV file (Time series must be in the last column)"),
+        gr.Slider(minimum=7, maximum=30, step=1, value=14, label="Number of Future Steps (Days) to Predict"),
+    ],
+    outputs=gr_plot,
+    title="Chronos Time Series Forecasting Demo on Hugging Face",
+    description="Upload a CSV file containing a single historical time series. This demo uses the Chronos-T5-Small Foundation Model to generate a 14-day (default) forecast.",
+    examples=None,
+    live=False,
+).launch()