app.py added

app.py

@@ -0,0 +1,112 @@
+import os
+
+import gradio as gr
+import tensorflow as tf
+import joblib
+import numpy as np
+import pandas as pd
+import yfinance as yf
+from huggingface_hub import hf_hub_download
+
+# This forces TensorFlow to only use the CPU.
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+# --- 1. Download Model and Scalers from Hugging Face Hub ---
+# FIX #1: Ensured the repository name is 100% correct.
+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)
+    scalers_path = hf_hub_download(repo_id=MODEL_REPO, filename=SCALER_FILENAME)
+    print("✅ Files downloaded successfully.")
+except Exception as e:
+    print(f"❌ Error downloading files: {e}")
+    model_path, scalers_path = None, None
+
+# --- 2. Load the Model and Scalers ---
+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(model_path)
+        print("✅ Model loaded successfully.")
+    except Exception as e:
+        print(f"❌ Error loading model: {e}")
+
+if scalers_path and os.path.exists(scalers_path):
+    try:
+        loaded_scalers = joblib.load(scalers_path)
+        print("✅ Scalers loaded successfully.")
+    except Exception as e:
+        print(f"❌ Error loading scalers: {e}")
+
+ticker_to_name = {'ZURVY': 'Zurich Insurance Group AG'}
+
+def get_ticker_from_input(input_name):
+    return input_name.upper()
+
+# --- 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. The backend may have failed to start."
+    # ... (The rest of the function is the same)
+    ticker = get_ticker_from_input(input_name)
+    if not ticker:
+        return "Error: Invalid stock ticker."
+
+    print(f"\n--- Generating forecast for {ticker} ---")
+    try:
+        data_df = yf.download(ticker, period="1y", progress=False)
+        if data_df.empty:
+             return f"Error: No data found for ticker {ticker}. It may be delisted or invalid."
+    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. 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)
+
+    scaler = scalers_dict.get(ticker)
+    if not scaler:
+        print(f"Warning: No specific scaler found for {ticker}. Using ZURVY's scaler as a fallback.")
+        scaler = scalers_dict.get('ZURVY')
+        if not scaler:
+            return "Error: Default scaler 'ZURVY' not 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
+
+# --- 4. Create the Gradio Interface ---
+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")
+
+# --- 5. Mount and Serve the React App's Static Files ---
+# This function requires a modern version of Gradio, specified in README.md
+app = gr.mount_static_directory(app, "build")
+
+# Launch the server
+if __name__ == "__main__":
+    app.launch()