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app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import joblib
+from tensorflow.keras.models import load_model
+from tensorflow.keras.metrics import MeanAbsoluteError
+from tensorflow.keras.losses import MeanSquaredError
+
+model = load_model("stock_model.h5", custom_objects={'mae': MeanAbsoluteError(), 'mse': MeanSquaredError()})
+scaler = joblib.load("scaler.pkl")
+
+
+FEATURES = ['open', 'high', 'low', 'close', 'volume', 'MA5', 'MA10', 'MA20']
+
+def predict_next_day(open_price, high_price, low_price, close_price, volume, ma5, ma10, ma20):
+    """
+    Predicts the next day's closing price using the trained LSTM model.
+    """
+    dummy_df = pd.DataFrame([[open_price, high_price, low_price, close_price, volume, ma5, ma10, ma20]],
+                            columns=FEATURES)
+
+    scaled_input = scaler.transform(dummy_df)
+
+
+    reshaped_input = scaled_input.reshape(1, 1, len(FEATURES))
+
+    scaled_prediction = model.predict(reshaped_input)[0][0]
+
+    # To get the actual price, we need to inverse transform the prediction.
+    # The scaler was trained on all features, so we need to create a dummy
+    # array with zeros for all other features. The 'close' price is at index 3.
+    # The inverse_close function from the original notebook is implemented here.
+    def inverse_close(scaled_value, close_index=3, total_features=len(FEATURES)):
+        dummy = np.zeros((1, total_features))
+        dummy[:, close_index] = scaled_value
+        return scaler.inverse_transform(dummy)[:, close_index]
+
+    # Inverse transform the scaled prediction to get the real price
+    predicted_price = inverse_close(scaled_prediction)[0]
+    
+    return f"${predicted_price:.2f}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_next_day,
+    inputs=[
+        gr.Number(label="Open Price", value=150.0),
+        gr.Number(label="High Price", value=155.0),
+        gr.Number(label="Low Price", value=149.0),
+        gr.Number(label="Close Price", value=152.0),
+        gr.Number(label="Volume", value=1000000),
+        gr.Number(label="5-Day MA", value=151.5),
+        gr.Number(label="10-Day MA", value=151.0),
+        gr.Number(label="20-Day MA", value=150.5),
+    ],
+    outputs=gr.Textbox(label="Predicted Next Day's Close Price"),
+    title="Stock Price Prediction",
+    description="Enter today's stock data to predict tomorrow's closing price."
+)
+
+iface.launch(debug=True)

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3db8e1f6408ec42285079039a7eb44ee8b555a936088eac3e9bffce2728689fe
+size 983

stock_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2627cfa3926dea2d334cd66e3f4491ca09c0e422795dc6d0766c0b65bced4866
+size 548424