app.py

import gradio as gr
from flask import Flask, render_template, request
import joblib
import numpy as np
import pickle


app = Flask(__name__)

# Load models
regressor = joblib.load('model/regressor_model.pkl')
classifier = joblib.load('model/classifier_model.pkl')

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/regression')
def regression():
    return render_template('regression.html')

@app.route('/classification')
def classification():
    return render_template('classification.html')

@app.route('/predict_regression', methods=['POST'])
def predict_regression():
    try:
        input_data = [float(x) for x in request.form.values()]
        features = np.array([input_data])
        prediction = regressor.predict(features)[0]
        return render_template('regression.html', prediction_text=f'📈 Predicted Value: {prediction:.2f}')
    except Exception as e:
        return render_template('regression.html', prediction_text=f"⚠️ Error: {str(e)}")

@app.route('/predict_classification', methods=['POST'])
def predict_classification():
    try:
        input_data = [float(x) for x in request.form.values()]
        features = np.array([input_data])
        prediction = classifier.predict(features)[0]
        return render_template('classification.html', prediction_text=f'🎯 Predicted Class: {int(prediction)}')
    except Exception as e:
        return render_template('classification.html', prediction_text=f"⚠️ Error: {str(e)}")

if __name__ == '__main__':
    app.run(debug=True)