src/app.py

import os
import json
from flask import Flask, render_template, request, jsonify, send_file
import pandas as pd

from src.model import ChurnClassifier
from src.predict import predict_single
from src.train import train_pipeline

app = Flask(__name__)

MODEL_PATH = "models/churn_model.pkl"
METRICS_PATH = "models/metrics.json"

@app.route("/")
def index():
    # Load metrics if available
    metrics = None
    if os.path.exists(METRICS_PATH):
        try:
            with open(METRICS_PATH, "r") as f:
                metrics = json.load(f)
        except Exception:
            pass
            
    model_exists = os.path.exists(MODEL_PATH)
    return render_template("index.html", metrics=metrics, model_exists=model_exists)

@app.route("/video")
def video():
    possible_paths = ["download.webm", "src/static/download.webm", os.path.join(os.path.dirname(__file__), "..", "download.webm")]
    for path in possible_paths:
        if os.path.exists(path):
            return send_file(path, mimetype="video/webm")
    return "Video not found", 404

@app.route("/train", methods=["POST"])
def train():
    try:
        train_pipeline(
            n_samples=1000,
            test_size=0.2,
            model_path=MODEL_PATH,
            metrics_path=METRICS_PATH
        )
        with open(METRICS_PATH, "r") as f:
            metrics = json.load(f)
        return jsonify({"success": True, "metrics": metrics})
    except Exception as e:
        return jsonify({"success": False, "error": str(e)}), 500

@app.route("/predict", methods=["POST"])
def predict():
    try:
        age = int(request.form.get("age", 40))
        monthly_charges = float(request.form.get("monthly_charges", 50.0))
        contract_length = int(request.form.get("contract_length", 12))
        support_calls = int(request.form.get("support_calls", 1))
        tech_support = request.form.get("tech_support", "no")
        
        result = predict_single(
            age=age,
            monthly_charges=monthly_charges,
            contract_length=contract_length,
            support_calls=support_calls,
            tech_support=tech_support,
            model_path=MODEL_PATH
        )
        return jsonify({"success": True, "result": result})
    except Exception as e:
        return jsonify({"success": False, "error": str(e)}), 500

if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860))
    app.run(host="0.0.0.0", port=port, debug=False)