app.py

import streamlit as st
import joblib
import numpy as np
import os
import zipfile

# Unzip the model file to current dir
if not os.path.exists("linear_model.pkl") and os.path.exists("model.zip"):
    with zipfile.ZipFile("model.zip", "r") as zip_ref:
        zip_ref.extractall()

# Load the model
model = joblib.load("linear_model.pkl")


# ✅ Streamlit UI
st.set_page_config(page_title="ML Model Predictor", layout="centered")
st.title("📊 Linear Regression Predictor")
st.markdown("Enter feature values to get a prediction from your trained model.")

# ✅ Update this to match your model's expected input features
num_features = 12

inputs = []
for i in range(num_features):
    val = st.number_input(f"Feature {i+1}", value=0.0)
    inputs.append(val)

if st.button("Predict"):
    input_array = np.array(inputs).reshape(1, -1)
    prediction = model.predict(input_array)[0]
    st.success(f"🔮 Prediction: {prediction}")