Update app.py

app.py

@@ -8,14 +8,10 @@ from huggingface_hub import hf_hub_download
 
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 
-
-
 MODEL_REPO = "munem420/stock-forecaster-lstm"
 MODEL_FILENAME = "model_lstm.h5"
 SCALER_FILENAME = "scalers.joblib"
 
-
-
 print("--- Downloading model and scalers ---")
 try:
     model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME)
@@ -28,7 +24,6 @@ except Exception as e:
 loaded_model_lstm = None
 loaded_scalers = None
 
-
 if model_path and os.path.exists(model_path):
     try:
         loaded_model_lstm = tf.keras.models.load_model(
@@ -48,73 +43,53 @@ if scalers_path and os.path.exists(scalers_path):
 
 ticker_to_name = {'ZURVY': 'Zurich Insurance Group AG'}
 
-
-
-
-
-
-
-
-
+# Example placeholder DataFrame (replace with your actual data)
+data_df = pd.DataFrame({
+    "Date": pd.date_range(start="2024-01-01", periods=100),
+    "Close": np.linspace(100, 200, 100)
+})
 
 def get_ticker_from_input(input_name):
-    return input_name.upper()
-
-
-
+    return input_name.upper().strip()
 
+def forecast_stock(input_name):
+    model = loaded_model_lstm
+    scalers_dict = loaded_scalers
+    input_width = 60
 
-
-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. 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} ---")
-
-
+        return "Error: Model or scalers not loaded."
 
-    
+    ticker = get_ticker_from_input(input_name)
     if len(data_df) < input_width:
         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(-input, 1)
+    close_prices = recent_data['Close'].values.reshape(-1, 1)
     scaler = scalers_dict.get(ticker)
+
     if not scaler:
-        print(f"Warning: No specific scaler found for {ticker}. Using ZURVY's scaler as a fallback.")
+        print(f"⚠️ No specific scaler for {ticker}. Using fallback.")
         scaler = scalers_dict.get('ZURVY')
         if not scaler:
-            return "Error: Default scaler 'ZURVY' not found."
+            return "Error: No default scaler found."
+
     scaled_data = scaler.transform(close_prices)
     X_pred = scaled_data.reshape(1, input_width, 1)
     prediction_scaled = model.predict(X_pred, verbose=0)[0][0]
     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}"
-    )
-    print(result)
-    return result
-
-def predict_api(ticker_symbol):
-    return forecast_stock(ticker_symbol, loaded_model_lstm, loaded_scalers)
-
-with gr.Blocks() as app:
-    gr.Markdown("This is the backend for the React Stock Forecaster App.")
-    ticker_input = gr.Textbox(label="Stock Ticker", visible=False)
-    output_text = gr.Textbox(label="Forecast", visible=False)
-    ticker_input.submit(predict_api, inputs=[ticker_input], outputs=[output_text], api_name="predict")
-
-app = gr.mount_static_directory(app, "build")
-
-
-
-
-
 
+    return f"Last close for {ticker}: ${last_close:.2f}\nPredicted next day close: ${prediction_actual:.2f}"
 
+# ✅ Simple Gradio interface
+iface = gr.Interface(
+    fn=forecast_stock,
+    inputs=gr.Textbox(label="Enter Ticker or Company Name"),
+    outputs=gr.Textbox(label="Predicted Next Day Close"),
+    title="Stock Price Forecaster (LSTM)",
+    description="Enter a stock ticker or company name to predict the next day's close price."
+)
 
 if __name__ == "__main__":
-    app.launch()
+    iface.launch()