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app.py

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+import streamlit as st
+import numpy as np
+import pickle
+
+# Function to load the model from pickle
+def load_model(path):
+    with open(path, 'rb') as f:
+        model = pickle.load(f)
+    return model
+
+# Define the main function for predicting stock price
+def predict_stock_price(open_val, high_val, low_val, close_val, adj_close_val, volume, model):
+    try:
+        # Prepare the input data as a numpy array
+        X = np.array([[open_val, high_val, low_val, close_val, adj_close_val]])
+
+        # Use the loaded model for prediction
+        predicted_price = model.predict(X)
+
+        return predicted_price[0]
+    except (ValueError, AttributeError) as e:
+        st.error(f'Error occurred: {str(e)}')
+        return None
+
+# Define the Streamlit app
+def main():
+    st.title('Stock Prediction')
+
+    # Load your trained model from pickle
+    model_path = 'model.pkl'  # Replace with your actual path
+    model = load_model(model_path)
+
+    # Input fields for user to enter stock data
+    open_val = st.number_input('Open (Range: 0 to 100000)', min_value=0.0, max_value=100000.0, value=0.0)
+    high_val = st.number_input('High (Range: 0 to 100000)', min_value=0.0, max_value=100000.0, value=0.0)
+    low_val = st.number_input('Low (Range: 0 to 100000)', min_value=0.0, max_value=100000.0, value=0.0)
+    close_val = st.number_input('Close* (Range: 0 to 100000)', min_value=0.0, max_value=100000.0, value=0.0)
+    adj_close_val = st.number_input('Adj Close** (Range: 0 to 100000)', min_value=0.0, max_value=100000.0, value=0.0)
+    volume = st.number_input('Volume (Range: 0 to 1,000,000,000)', min_value=0.0, max_value=1000000000.0, value=0.0)
+
+    # Predict button
+    if st.button('Predict'):
+        if any([open_val == 0.0, high_val == 0.0, low_val == 0.0, close_val == 0.0, adj_close_val == 0.0, volume == 0.0]):
+            st.error('Please enter valid numeric values for all fields.')
+        else:
+            # Call prediction function
+            predicted_price = predict_stock_price(open_val, high_val, low_val, close_val, adj_close_val, volume, model)
+            
+            if predicted_price is not None:
+                st.success(f'The predicted stock price is: {predicted_price:.2f}')
+            else:
+                st.error('Failed to predict stock price.')
+
+if __name__ == '__main__':
+    main()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:41da2ec13c1ba813e5dc65951ca56417c0e5ec419115b0021a425d31d25a44d2
+size 613

requirements.txt

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+pandas
+scikit-learn
+numpy
+streamlit