app.py

import gradio as gr
import tensorflow as tf
import cv2
import numpy as np
import pickle
import mediapipe as mp
import os

# ✅ Load Face Detector
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

# ✅ Load Model and Custom Dropout Layer
class MCDropout(tf.keras.layers.Dropout):
    def call(self, inputs):
        return super().call(inputs, training=True)

# ✅ Load the Saved Model
MODEL_PATH = "facial_recogn_model.keras"
model = tf.keras.models.load_model(MODEL_PATH, custom_objects={"MCDropout": MCDropout}, compile=False)

# ✅ Load Registered Users
USER_DATABASE = "registered_users.pkl"
if os.path.exists(USER_DATABASE):
    with open(USER_DATABASE, "rb") as f:
        registered_users = pickle.load(f)
else:
    registered_users = {}

# ✅ Function to Register a User
def register_user(username, image):
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Convert from RGB to BGR
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    if len(faces) == 0:
        return "❌ No face detected! Try again."

    x, y, w, h = faces[0]
    face = gray[y:y+h, x:x+w]
    face = cv2.resize(face, (160, 160)) / 255.0  # Normalize
    face = face.reshape(1, 160, 160, 1)

    registered_users[username] = model.predict(face)[0]  # Store as a 1D array
    
    # Save to file
    with open(USER_DATABASE, "wb") as f:
        pickle.dump(registered_users, f)

    return f"✅ {username} registered successfully!"

# ✅ Cosine Similarity Function
def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# ✅ Function to Authenticate User
def authenticate_user(image):
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    if len(faces) == 0:
        return "❌ No face detected! Try again."

    x, y, w, h = faces[0]
    face = gray[y:y+h, x:x+w]
    face = cv2.resize(face, (160, 160)) / 255.0  # Normalize
    face = face.reshape(1, 160, 160, 1)

    user_embedding = model.predict(face)[0]

    for username, saved_embedding in registered_users.items():
        similarity = cosine_similarity(user_embedding, saved_embedding)
        if similarity > 0.85:  # Threshold for authentication
            return f"✅ Welcome, {username}!"

    return "❌ Authentication Failed!"

# ✅ Gesture Control Function
def gesture_control(image):
    mp_hands = mp.solutions.hands
    hands = mp_hands.Hands(min_detection_confidence=0.7, min_tracking_confidence=0.7)
    mp_draw = mp.solutions.drawing_utils

    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    frame_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    result = hands.process(frame_rgb)

    if result.multi_hand_landmarks:
        for hand_landmarks in result.multi_hand_landmarks:
            mp_draw.draw_landmarks(image, hand_landmarks, mp_hands.HAND_CONNECTIONS)

            index_tip = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x
            thumb_tip = hand_landmarks.landmark[mp_hands.HandLandmark.THUMB_TIP].x
            wrist = hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].x

            if thumb_tip > wrist + 0.1:
                return "💡 Lights ON"
            elif thumb_tip < wrist - 0.1:
                return "💡 Lights OFF"
            elif index_tip > wrist + 0.2:
                return "🚪 Door Opening"
            elif index_tip < wrist - 0.2:
                return "🚪 Door Closing"

    return "🤖 No gesture detected"

# ✅ Gradio Interface
iface = gr.Interface(
    fn=lambda action, username, image: (
        register_user(username, image) if action == "Register"
        else authenticate_user(image) if action == "Authenticate"
        else gesture_control(image)
    ),
    inputs=[
        gr.Radio(["Register", "Authenticate", "Gesture Control"], label="Select Action"),
        gr.Textbox(label="Enter Username (For Registration Only)"),
        gr.Image(type="numpy", label="Upload Image")
    ],
    outputs=gr.Textbox(label="Result"),
    title="Face Authentication & Gesture Control",
    description="🔐 Register, Authenticate Users & Control Devices via Hand Gestures."
)

# ✅ Launch the Gradio App
iface.launch()