Create app.py

app.py

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+import streamlit as st
+import joblib
+import numpy as np
+
+# Load the model from Hugging Face Model Hub (replace with your model's Hugging Face repository URL)
+model = joblib.load('random_forest_model.pkl')  # If model is uploaded directly in the Space, this works.
+
+# Streamlit App Title
+st.title("Power Prediction App")
+st.subheader("Enter the values for Current (I) and Resistance (R) to predict Power (P)")
+
+# Input fields for Current (I) and Resistance (R)
+current = st.number_input("Current (I in Amps)", min_value=0.1, max_value=10.0, value=5.0, step=0.1)
+resistance = st.number_input("Resistance (R in Ohms)", min_value=1.0, max_value=100.0, value=50.0, step=1.0)
+
+# Button to make prediction
+if st.button("Predict Power"):
+    # Predict the power using the trained model
+    prediction = model.predict([[current, resistance]])
+    
+    # Display the result
+    st.write(f"Predicted Power (P) for I = {current} A and R = {resistance} Ω is: {prediction[0]:.2f} Watts")