Create app.py

app.py

@@ -0,0 +1,32 @@
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import tensorflow as tf
+import numpy as np
+import pickle
+
+app = Flask(__name__)
+CORS(app)  # Allow frontend (CodePen) to access this API
+
+# Load trained LSTM model
+model = tf.keras.models.load_model("nse_lstm_model_fixed.h5")  # Update with correct model path
+
+# Load MinMaxScaler for inverse transformation
+with open("close_price_scaler.pkl", "rb") as f:
+    close_scaler = pickle.load(f)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        data = request.json['features']  # Receive stock data from frontend
+        input_data = np.array(data).reshape(1, 60, 7)  # Ensure correct shape
+        
+        # Make prediction
+        prediction = model.predict(input_data)
+        predicted_price = close_scaler.inverse_transform(prediction.reshape(-1, 1)).flatten()[0]
+
+        return jsonify({'predicted_price': round(predicted_price, 2)})
+
+    except Exception as e:
+        return jsonify({'error': str(e)})
+        if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)