import json
import os
import subprocess
import threading
import time
import numpy as np
import tensorflow as tf
from flask import Flask, render_template, jsonify, request
from werkzeug.utils import secure_filename

app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'uploads'
os.makedirs('uploads', exist_ok=True)

training_status = {
    "running": False,
    "finished": False
}


def run_training(file_path):
    training_status["running"] = True
    training_status["finished"] = False

    with open("training_results.json", "w") as f:
        json.dump([], f)

    server = subprocess.Popen(["python3", "server.py", file_path])
    time.sleep(3)
    client0 = subprocess.Popen(["python3", "client.py", "0", file_path])
    client1 = subprocess.Popen(["python3", "client.py", "1", file_path])

    server.wait()
    client0.wait()
    client1.wait()

    training_status["running"] = False
    training_status["finished"] = True


@app.route("/")
def index():
    return render_template("landing.html")


@app.route("/dashboard")
def dashboard():
    return render_template("index.html")


@app.route("/predict")
def predict():
    return render_template("predict.html")


@app.route("/predict_result", methods=["POST"])
def predict_result():
    diagnosis_type = request.form.get("diagnosis_type", "diabetes")

    if diagnosis_type == "diabetes":
        features = [
            float(request.form.get("pregnancies", 0)),
            float(request.form.get("glucose", 0)),
            float(request.form.get("blood_pressure", 0)),
            float(request.form.get("skin_thickness", 0)),
            float(request.form.get("insulin", 0)),
            float(request.form.get("bmi", 0)),
            float(request.form.get("diabetes_pedigree", 0)),
            float(request.form.get("age", 0)),
        ]
        model_path = "diabetes_model.keras"
        disease_name = "Diabetes"

    elif diagnosis_type == "heart":
        features = [
            float(request.form.get("age", 0)),
            float(request.form.get("sex", 0)),
            float(request.form.get("cp", 0)),
            float(request.form.get("trestbps", 0)),
            float(request.form.get("chol", 0)),
            float(request.form.get("fbs", 0)),
            float(request.form.get("restecg", 0)),
            float(request.form.get("thalach", 0)),
            float(request.form.get("exang", 0)),
            float(request.form.get("oldpeak", 0)),
            float(request.form.get("slope", 0)),
            float(request.form.get("ca", 0)),
            float(request.form.get("thal", 0)),
        ]
        model_path = "heart_model.keras"
        disease_name = "Heart Disease"

    else:
        return jsonify({"error": "Invalid diagnosis type"})

    X = np.array([features])
    model = tf.keras.models.load_model(model_path)
    prediction = model.predict(X)[0][0]

    risk = "High Risk" if prediction > 0.5 else "Low Risk"
    confidence = round(float(prediction) * 100, 1) if prediction > 0.5 else round((1 - float(prediction)) * 100, 1)

    return jsonify({
        "risk": risk,
        "confidence": confidence,
        "probability": round(float(prediction) * 100, 1),
        "disease": disease_name
    })


@app.route("/start_training", methods=["POST"])
def start_training():
    if training_status["running"]:
        return jsonify({"status": "already running"})

    file_path = "diabetes.csv"

    if "file" in request.files and request.files["file"].filename != "":
        file = request.files["file"]
        filename = secure_filename(file.filename)
        file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        file.save(file_path)

    thread = threading.Thread(target=run_training, args=(file_path,))
    thread.start()
    return jsonify({"status": "started"})


@app.route("/status")
def status():
    results = []
    if os.path.exists("training_results.json"):
        with open("training_results.json", "r") as f:
            results = json.load(f)
    return jsonify({
        "running": training_status["running"],
        "finished": training_status["finished"],
        "rounds": results
    })


if __name__ == "__main__":
    app.run(debug=False, port=8000)