from flask import Flask, request, jsonify
from flask_cors import CORS
import face_recognition
import os
import numpy as np
import pickle

app = Flask(__name__)
CORS(app)
#pip install Flask flask-cors face_recognition

# Directory to save student images and encodings
STUDENT_IMAGES_DIR = 'student_images'
STUDENT_ENCODINGS_FILE = 'student_encodings.pkl'

# Ensure the directory exists
os.makedirs(STUDENT_IMAGES_DIR, exist_ok=True)

# Load existing encodings if available
if os.path.exists(STUDENT_ENCODINGS_FILE):
    with open(STUDENT_ENCODINGS_FILE, 'rb') as f:
        student_encodings = pickle.load(f)
else:
    student_encodings = {}

@app.route('/upload_images', methods=['POST'])
def upload_images():
    student_id = request.form.get('student_id')
    if 'images' not in request.files or not student_id:
        return jsonify({"error": "No images or student ID provided"}), 400

    images = request.files.getlist('images')
    encodings = []

    for image in images:
        image_path = os.path.join(STUDENT_IMAGES_DIR, f"{student_id}_{image.filename}")
        image.save(image_path)

        # Load the image and get the face encodings
        img = face_recognition.load_image_file(image_path)
        img_encodings = face_recognition.face_encodings(img)

        if img_encodings:
            encodings.append(img_encodings[0])

    if encodings:
        student_encodings[student_id] = np.mean(encodings, axis=0)

        # Save the encodings to file
        with open(STUDENT_ENCODINGS_FILE, 'wb') as f:
            pickle.dump(student_encodings, f)

        return jsonify({"message": "Images uploaded and processed successfully"}), 200
    else:
        return jsonify({"error": "No faces found in the uploaded images"}), 400

@app.route('/recognize', methods=['POST'])
def recognize():
    if 'image' not in request.files:
        return jsonify({"error": "No image provided"}), 400

    image = request.files['image']
    img = face_recognition.load_image_file(image)
    img_encodings = face_recognition.face_encodings(img)

    if img_encodings:
        img_encoding = img_encodings[0]
        distances = {student_id: np.linalg.norm(encoding - img_encoding) for student_id, encoding in student_encodings.items()}
        student_id = min(distances, key=distances.get)

        return jsonify({"student_id": student_id}), 200
    else:
        return jsonify({"error": "No face found in the uploaded image"}), 400

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