import streamlit as st
from PIL import Image
import numpy as np
import cv2
import face_recognition
import os
from typing import List, Tuple
import requests
from urllib.parse import quote
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class FaceRecognitionApp:
    def __init__(self, image_directory: str = "facerecognition"):
        """Initialize the face recognition application."""
        self.image_directory = image_directory
        self.images: List[np.ndarray] = []
        self.classnames: List[str] = []
        self.encode_list_known = None
        
        # Initialize Streamlit page
        st.set_page_config(page_title="Face Recognition System", page_icon="👤")
        st.title("Face Recognition System")
        
        # Load and encode known faces
        self._load_known_faces()

    def _load_known_faces(self) -> None:
        """Load and encode all known faces from the directory and predefined images."""
        try:
            # Load predefined images
            predefined_images = {
                "sarwan.jpg": "Sarwan",
                "rattantata.png": "RattanTata",
                "Ravinder.jpg": "RavinderKaur"
            }
            
            for img_path, name in predefined_images.items():
                if os.path.exists(img_path):
                    img = cv2.imread(img_path)
                    if img is not None:
                        self.images.append(img)
                        self.classnames.append(name)
                    else:
                        logger.warning(f"Failed to load image: {img_path}")

            # Load images from directory
            if os.path.exists(self.image_directory):
                for filename in os.listdir(self.image_directory):
                    if filename.lower().endswith(('.jpg', '.jpeg', '.png')):
                        img_path = os.path.join(self.image_directory, filename)
                        img = cv2.imread(img_path)
                        if img is not None:
                            self.images.append(img)
                            self.classnames.append(os.path.splitext(filename)[0])
                        else:
                            logger.warning(f"Failed to load image: {img_path}")
            
            # Encode faces
            if self.images:
                self.encode_list_known = []
                for img in self.images:
                    rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
                    encodings = face_recognition.face_encodings(rgb_img)
                    if encodings:
                        self.encode_list_known.append(encodings[0])
                    else:
                        logger.warning("No face detected in one of the reference images")
            
            logger.info(f"Loaded {len(self.images)} images for face recognition")
            
        except Exception as e:
            logger.error(f"Error loading known faces: {str(e)}")
            st.error("Error loading reference images. Please check the image files.")

    def _process_face(self, image: np.ndarray, scale: float = 0.25) -> Tuple[np.ndarray, bool]:
        """Process image and detect faces."""
        img_small = cv2.resize(image, (0, 0), None, scale, scale)
        img_rgb = cv2.cvtColor(img_small, cv2.COLOR_BGR2RGB)
        
        face_locations = face_recognition.face_locations(img_rgb)
        face_encodings = face_recognition.face_encodings(img_rgb, face_locations)
        
        face_matched = False
        
        for encoding, (top, right, bottom, left) in zip(face_encodings, face_locations):
            if self.encode_list_known:
                matches = face_recognition.compare_faces(self.encode_list_known, encoding)
                face_distances = face_recognition.face_distance(self.encode_list_known, encoding)
                
                if any(matches):
                    best_match_idx = np.argmin(face_distances)
                    if matches[best_match_idx]:
                        name = self.classnames[best_match_idx].upper()
                        face_matched = True
                        
                        # Scale back the coordinates
                        top, right, bottom, left = [coord * int(1/scale) for coord in (top, right, bottom, left)]
                        
                        # Draw rectangle and name
                        cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
                        cv2.rectangle(image, (left, bottom - 35), (right, bottom), (0, 255, 0), cv2.FILLED)
                        cv2.putText(image, name, (left + 6, bottom - 6), 
                                  cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
                        
                        # Update external service
                        self._update_external_service(name)
        
        return image, face_matched

    def _update_external_service(self, name: str) -> None:
        """Update external service with recognition results."""
        try:
            base_url = "https://fc11.glitch.me/submit"
            params = {
                "email": "pm",
                "message": "faceReco",
                "name": quote(name)
            }
            
            response = requests.get(base_url, params=params, timeout=5)
            response.raise_for_status()
            
            st.success(f"Successfully updated recognition for {name}")
            logger.info(f"External service updated for {name}")
            
        except requests.exceptions.RequestException as e:
            logger.error(f"Failed to update external service: {str(e)}")
            st.warning("Failed to update external service, but recognition completed successfully")

    def run(self) -> None:
        """Run the face recognition application."""
        if not self.encode_list_known:
            st.error("No reference faces loaded. Please check the image directory.")
            return

        img_file_buffer = st.camera_input("Take Your Picture")
        
        if img_file_buffer is not None:
            try:
                image = np.array(Image.open(img_file_buffer))
                processed_image, face_matched = self._process_face(image.copy())
                
                st.image(processed_image, use_column_width=True, channels="BGR")
                
                if not face_matched:
                    st.warning("No matching faces found in the image.")
                    
            except Exception as e:
                logger.error(f"Error processing image: {str(e)}")
                st.error("Error processing the image. Please try again.")

if __name__ == "__main__":
    app = FaceRecognitionApp()
    app.run()