Add application file

app.py

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+# app.py
+import gradio as gr
+import json
+import traceback
+
+# Import necessary functions and the model class from your original script
+# Make sure predict_stock_prices.py is in the same directory
+from predict_stock_prices import (
+    BiLSTMModel, # Need to import the class for joblib/torch to load model correctly
+    predict_stock_prices,
+    batch_predict_to_json # Assuming this function takes the list and paths
+)
+
+# --- Model Configuration ---
+# These paths should correspond to the files uploaded to your Hugging Face Space
+MODEL_PATH = "bilstm_stock_model.pth"
+SCALER_PATH = "scaler_diff.pkl"
+METADATA_PATH = "model_metadata.pkl"
+
+# --- Gradio Interface Function ---
+def run_prediction(ticker_string):
+    """
+    Takes a comma-separated string of tickers, runs prediction,
+    and returns the result as a JSON object or error string.
+    """
+    if not ticker_string:
+        return {"error": "Please enter at least one ticker symbol."}
+
+    # Split string into a list of tickers, removing whitespace
+    tickers = [ticker.strip().upper() for ticker in ticker_string.split(',') if ticker.strip()]
+
+    if not tickers:
+        return {"error": "No valid ticker symbols entered."}
+
+    print(f"Received request for tickers: {tickers}") # Log received tickers
+
+    try:
+        # Call your existing batch prediction function
+        # It already returns a dictionary suitable for JSON output
+        predictions = batch_predict_to_json(
+            ticker_symbols=tickers,
+            model_path=MODEL_PATH,
+            scaler_path=SCALER_PATH,
+            metadata_path=METADATA_PATH
+        )
+        print(f"Prediction successful for: {list(predictions.keys())}") # Log success
+        # Check for errors within the prediction results
+        errors = {k:v for k,v in predictions.items() if isinstance(v, dict) and 'error' in v}
+        if errors:
+             print(f"Errors occurred during prediction: {errors}") # Log errors
+        return predictions # Return the entire dictionary
+
+    except FileNotFoundError as e:
+         print(f"Error: Model file not found - {e}")
+         return {"error": f"Required file not found: {e}. Ensure model, scaler, and metadata files are uploaded correctly."}
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+        traceback.print_exc() # Print detailed traceback to logs
+        return {"error": f"An unexpected error occurred: {str(e)}"}
+
+# --- Build Gradio Interface ---
+# Use Markdown for a richer description
+description = """
+## BiLSTM Stock Price Predictor (-15y / +15y)
+
+Enter one or more stock ticker symbols (e.g., `AAPL`, `MSFT`, `GOOGL`), separated by commas.
+The model will fetch historical data, predict future prices for the next 15 years using a BiLSTM model combined with Geometric Brownian Motion (GBM),
+and return the historical data for the past 15 years (or less if unavailable) combined with the predictions.
+
+**Note:**
+*   Predictions are based on historical 'Close' prices and involve inherent uncertainty. **This is not financial advice.**
+*   Fetching data and running predictions might take a moment, especially for multiple tickers.
+*   Ensure ticker symbols are valid on Yahoo Finance.
+"""
+
+iface = gr.Interface(
+    fn=run_prediction,
+    inputs=gr.Textbox(
+        lines=1,
+        placeholder="Enter Ticker Symbols (e.g., AAPL, MSFT, GOOGL)",
+        label="Ticker Symbols (comma-separated)"
+    ),
+    outputs=gr.JSON(label="Prediction Results (Historical + Future Prices)"),
+    title="Stock Price Prediction",
+    description=description,
+    examples=[["AAPL"], ["MSFT,GOOGL,NVDA"]],
+    allow_flagging='never' # Optional: Disable flagging
+)
+
+# --- Launch the App ---
+if __name__ == "__main__":
+    iface.launch() # Share=True is not needed when deploying on Spaces