app.py

import gradio as gr
import torch
import numpy as np
from scipy.spatial.distance import cosine
import cv2
import os

RECOGNITION_THRESHOLD = 0.3

# Assuming the PyTorch model is a ResNet (or similar) and has been trained accordingly
# Load the embedding model
embedding_model = torch.load('full_mode2.pth')
embedding_model.eval()  # Set the model to evaluation mode

# Database to store embeddings and user IDs
user_embeddings = {}

# Preprocess the image
def preprocess_image(image):
    image = cv2.resize(image, (375, 375))  # Resize image
    image = image / 255.0  # Normalize pixel values
    image = np.transpose(image, (2, 0, 1))  # Change from HWC to CHW format
    return torch.tensor(image, dtype=torch.float32).unsqueeze(0)  # Add batch dimension

# Generate embedding
def generate_embedding(image):
    preprocessed_image = preprocess_image(image)
    with torch.no_grad():  # No need to track gradients
        return embedding_model(preprocessed_image).numpy()[0]

# Register new user
def register_user(image, user_id):
    try:
        embedding = generate_embedding(image)
        user_embeddings[user_id] = embedding
        return f"User {user_id} registered successfully."
    except Exception as e:
        return f"Error during registration: {str(e)}"

# Recognize user
def recognize_user(image):
    try:
        new_embedding = generate_embedding(image)
        min_distance = float('inf')
        recognized_user_id = "Unknown"
        
        for user_id, embedding in user_embeddings.items():
            distance = cosine(new_embedding, embedding)
            print(f"Distance for {user_id}: {distance}")  # Debug: Print distances
            if distance < min_distance:
                min_distance = distance
                recognized_user_id = user_id
        
        print(f"Min distance: {min_distance}")  # Debug: Print minimum distance
        if min_distance > RECOGNITION_THRESHOLD:
            return "User not recognized."
        else:
            return f"Recognized User: {recognized_user_id}"
    except Exception as e:
        return f"Error during recognition: {str(e)}"

        
def main():
    with gr.Blocks() as demo:
        gr.Markdown("Facial Recognition System")
        
        with gr.Tab("Register"):
            with gr.Row():
                img_register = gr.Image()
                user_id = gr.Textbox(label="User ID")
                register_button = gr.Button("Register")
            register_output = gr.Textbox()
            register_button.click(register_user, inputs=[img_register, user_id], outputs=register_output)

        with gr.Tab("Recognize"):
            with gr.Row():
                img_recognize = gr.Image()
                recognize_button = gr.Button("Recognize")
            recognize_output = gr.Textbox()
            recognize_button.click(recognize_user, inputs=[img_recognize], outputs=recognize_output)

    demo.launch(share=True)

if __name__ == "__main__":
    main()