app.py

import os
import cv2
import dlib
import numpy as np
import tensorflow as tf
import mediapipe as mp
from flask import Flask, render_template, Response

# Initialize Flask App
app = Flask(__name__)

# Load Face Detector (Dlib)
detector = dlib.get_frontal_face_detector()

# Load Pretrained Model for Skin Analysis (Placeholder)
model_path = "skin_model.h5"
if os.path.exists(model_path):
    skin_model = tf.keras.models.load_model(model_path)
else:
    skin_model = None

# OpenCV Video Capture
cap = cv2.VideoCapture(0)

# Function to Analyze Skin
def analyze_skin(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = detector(gray)
    for face in faces:
        x, y, w, h = face.left(), face.top(), face.width(), face.height()
        face_crop = frame[y:y+h, x:x+w]
        if skin_model:
            face_crop = cv2.resize(face_crop, (224, 224)) / 255.0
            prediction = skin_model.predict(np.expand_dims(face_crop, axis=0))
            return f"Skin Condition Score: {prediction[0][0]:.2f}"
    return "No face detected"

# Video Stream Function
def generate_frames():
    while True:
        success, frame = cap.read()
        if not success:
            break
        else:
            skin_result = analyze_skin(frame)
            cv2.putText(frame, skin_result, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
            ret, buffer = cv2.imencode('.jpg', frame)
            frame = buffer.tobytes()
            yield (b'--frame\r\n'
                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')

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

@app.route('/video_feed')
def video_feed():
    return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')

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