Create app.py

app.py

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+
+
+
+# app.py (for Hugging Face Spaces deployment)
+
+import streamlit as st
+import pickle
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+# Load the model
+def load_model():
+    with open("rf_model.pkl", "rb") as f:
+        model = pickle.load(f)
+    return model
+
+# Make predictions with the model
+def predict(model, new_data):
+    scaler = StandardScaler()
+    new_data_scaled = scaler.fit_transform(new_data)  # Standardize the data
+    return model.predict(new_data_scaled)
+
+# Streamlit app UI
+def main():
+    st.title('Random Forest Prediction App')
+
+    st.write("This app uses a pre-trained Random Forest model to make predictions.")
+
+    # Input data
+    feature1 = st.number_input("Feature 1", min_value=0.0, max_value=10.0, value=5.0)
+    feature2 = st.number_input("Feature 2", min_value=0.0, max_value=10.0, value=3.0)
+    feature3 = st.number_input("Feature 3", min_value=0.0, max_value=10.0, value=7.0)
+    feature4 = st.number_input("Feature 4", min_value=0.0, max_value=10.0, value=6.0)
+    feature5 = st.number_input("Feature 5", min_value=0.0, max_value=10.0, value=4.0)
+
+    # Prepare input data
+    new_data = np.array([[feature1, feature2, feature3, feature4, feature5]])
+
+    # Load the model and make predictions
+    model = load_model()
+    if st.button('Predict'):
+        if model:
+            predictions = predict(model, new_data)
+            st.write(f"Predictions: {predictions}")
+        else:
+            st.write("Model not loaded correctly.")
+
+if __name__ == '__main__':
+    main()