Update app.py

app.py

@@ -8,9 +8,9 @@ import yfinance as yf
 from huggingface_hub import hf_hub_download
 
 # --- 1. Download Model and Scalers from Hugging Face Hub ---
-# This is better than manually uploading them. The Space will fetch them automatically.
 MODEL_REPO = "munem420/stock-forecaster-lstm"
-MODEL_FILENAME = "model_lstm.h5"
+# FIX #1: Corrected model filename from .h5 to .keras
+MODEL_FILENAME = "model_lstm.keras"
 SCALER_FILENAME = "scalers.joblib"
 
 print("--- Downloading model and scalers ---")
@@ -20,6 +20,7 @@ try:
     print("✅ Files downloaded successfully.")
 except Exception as e:
     print(f"❌ Error downloading files: {e}")
+    # Exit gracefully if files can't be downloaded
     model_path, scalers_path = None, None
 
 # --- 2. Load the Model and Scalers ---
@@ -28,6 +29,7 @@ loaded_scalers = None
 
 if model_path and os.path.exists(model_path):
     try:
+        # No custom_objects needed for the .keras format in this case
         loaded_model_lstm = tf.keras.models.load_model(model_path)
         print("✅ Model loaded successfully.")
     except Exception as e:
@@ -40,26 +42,21 @@ if scalers_path and os.path.exists(scalers_path):
     except Exception as e:
         print(f"❌ Error loading scalers: {e}")
 
-# This dictionary is part of the original model's logic.
-# A more robust solution would fetch this dynamically or store it better.
 ticker_to_name = {'ZURVY': 'Zurich Insurance Group AG'}
 
 def get_ticker_from_input(input_name):
-    # Simplified version for this app
     return input_name.upper()
 
-# --- 3. The Main Forecasting Function (Adapted from your code) ---
+# --- 3. The Main Forecasting Function ---
 def forecast_stock(input_name, model, scalers_dict, input_width=60):
     if not model or not scalers_dict:
-        return "Error: Model or scalers not loaded."
+        return "Error: Model or scalers not loaded. The backend may have failed to start."
 
     ticker = get_ticker_from_input(input_name)
     if not ticker:
         return "Error: Invalid stock ticker."
 
     print(f"\n--- Generating forecast for {ticker} ---")
-
-    # Fetch recent data using yfinance
     try:
         data_df = yf.download(ticker, period="1y", progress=False)
         if data_df.empty:
@@ -67,16 +64,12 @@ def forecast_stock(input_name, model, scalers_dict, input_width=60):
     except Exception as e:
         return f"Error fetching data for {ticker}: {e}"
 
-
     if len(data_df) < input_width:
-        return f"Error: Not enough historical data for {ticker}. Need {input_width} days, but only have {len(data_df)}."
+        return f"Error: Not enough historical data. Need {input_width} days, but only have {len(data_df)}."
 
     recent_data = data_df.tail(input_width)
     close_prices = recent_data['Close'].values.reshape(-1, 1)
 
-    # Note: The original scalers were trained on specific stocks.
-    # Using a scaler for a different stock (e.g., AAPL) on a new ticker might not be accurate.
-    # For this example, we'll try to find a matching scaler or default to a common one.
     scaler = scalers_dict.get(ticker)
     if not scaler:
         print(f"Warning: No specific scaler found for {ticker}. Using ZURVY's scaler as a fallback.")
@@ -91,7 +84,6 @@ def forecast_stock(input_name, model, scalers_dict, input_width=60):
     prediction_actual = scaler.inverse_transform(np.array([[prediction_scaled]]))[0][0]
 
     last_close = recent_data['Close'].iloc[-1]
-
     result = (
         f"Last known close for {ticker}: ${last_close:.2f}\n"
         f"Predicted next day's close price: ${prediction_actual:.2f}"
@@ -100,27 +92,19 @@ def forecast_stock(input_name, model, scalers_dict, input_width=60):
     return result
 
 # --- 4. Create the Gradio Interface ---
-# We create a simple function that Gradio can expose as an API endpoint.
 def predict_api(ticker_symbol):
     return forecast_stock(ticker_symbol, loaded_model_lstm, loaded_scalers)
 
-# We use a dummy Gradio interface because we only need its backend API capabilities.
-# The `gr.Blocks()` allows us to run the server without displaying a UI.
 with gr.Blocks() as app:
     gr.Markdown("This is the backend for the React Stock Forecaster App.")
-    # This creates an API endpoint at `/run/predict`
     ticker_input = gr.Textbox(label="Stock Ticker", visible=False)
     output_text = gr.Textbox(label="Forecast", visible=False)
-    
-    # The Gradio API function must be tied to an event
-    # We will call this endpoint from our React app.
     ticker_input.submit(predict_api, inputs=[ticker_input], outputs=[output_text], api_name="predict")
 
 # --- 5. Mount and Serve the React App's Static Files ---
-# Before running this, you must build your React app using `npm run build`.
-# This will create a `build` directory with static files.
-# Gradio will serve the `index.html` from this directory.
-app.mount_static_directory("./build")
+# FIX #2: Changed to the correct function call format for Gradio
+app = gr.mount_static_directory(app, "build")
 
 # Launch the server
-app.launch()
+if __name__ == "__main__":
+    app.launch()