Critically analyze the following streamlit python code and provide your insights:

import streamlit as st
import os
import shutil
import cv2
import numpy as np
import insightface
from insightface.app import FaceAnalysis
import tkinter
from tkinter import filedialog
import math
import logging
from datetime import datetime
from concurrent.futures import ProcessPoolExecutor, as_completed
from tqdm import tqdm
import warnings
import plotly.graph_objects as go
from PIL import Image as PILImage
import time

warnings.filterwarnings('ignore', category=UserWarning)

class ImageProcessor:
    def __init__(self, thumbnails_folder, processed_thumbnails_folder):
        self.thumbnails_folder = thumbnails_folder
        self.processed_thumbnails_folder = processed_thumbnails_folder
        self.face_analyzer = self._initialize_face_analyzer()
        self._setup_logging()
        self._ensure_output_directory()

    def _initialize_face_analyzer(self):
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            app = FaceAnalysis(
                name='buffalo_l',
                providers=['CPUExecutionProvider']
            )
            app.prepare(ctx_id=-1, det_size=(640, 640))
            return app

    def _setup_logging(self):
        log_file = f'image_processing_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log'
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(levelname)s - %(message)s',
            handlers=[
                logging.FileHandler(log_file),
                logging.StreamHandler()
            ]
        )

    def _ensure_output_directory(self):
        if not os.path.exists(self.processed_thumbnails_folder):
            os.makedirs(self.processed_thumbnails_folder)
            logging.info(f"Created output directory: {self.processed_thumbnails_folder}")

    def get_face_orientation(self, face):
        kps = face.kps
        left_eye = kps[0]
        right_eye = kps[1]
        dy = right_eye[1] - left_eye[1]
        dx = right_eye[0] - left_eye[0]
        angle = math.degrees(math.atan2(dy, dx))
        
        if angle < 0:
            angle += 360
            
        angle_map = {0: 0, 90: 90, 180: 180, 270: 270}
        nearest_90 = min(angle_map.keys(), key=lambda x: abs((angle - x + 180) % 360 - 180))
        return angle_map[nearest_90]

    def rotate_image(self, image, angle):
        height, width = image.shape[:2]
        if angle in [90, 270]:
            new_width, new_height = height, width
        else:
            new_width, new_height = width, height

        center = (width // 2, height // 2)
        rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)

        if angle == 90:
            rotation_matrix[0, 2] += (height - width) / 2
            rotation_matrix[1, 2] += (height - width) / 2
        elif angle == 270:
            rotation_matrix[0, 2] += (height - width) / 2
            rotation_matrix[1, 2] += (width - height) / 2

        rotated_image = cv2.warpAffine(
            image,
            rotation_matrix,
            (new_width, new_height),
            flags=cv2.INTER_LINEAR,
            borderMode=cv2.BORDER_CONSTANT,
            borderValue=(0, 0, 0)
        )
        return rotated_image

    def process_thumbnails(self):
        supported_formats = ('.jpg', '.jpeg', '.png', '.bmp', '.tiff')
        image_files = [f for f in os.listdir(self.thumbnails_folder) if f.lower().endswith(supported_formats)]
        
        if not image_files:
            st.error(f"No supported images found in {self.thumbnails_folder}")
            return

        successful = 0
        failed = 0
        
        # Create container for progress tracking
        progress_container = st.container()
        with progress_container:
            progress_bar = st.progress(0)
            status_text = st.empty()

        # Create container for image display
        image_container = st.container()
        
        for idx, image_file in enumerate(image_files):
            with image_container:
                st.markdown("---")  # Horizontal rule
                st.markdown(f"**Image {idx + 1}/{len(image_files)}: {image_file}**")
                
                col1, col2 = st.columns(2)
                
                input_path = os.path.join(self.thumbnails_folder, image_file)
                output_path = os.path.join(self.processed_thumbnails_folder, f"processed_{image_file}")
                
                try:
                    image = cv2.imread(input_path)
                    if image is None:
                        failed += 1
                        continue

                    # Display original image
                    with col1:
                        st.markdown("**Original Image**")
                        st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), use_container_width=True)

                    faces = self.face_analyzer.get(image)
                    if not faces:
                        cv2.imwrite(output_path, image)
                        with col2:
                            st.markdown("**Processed Image (No rotation needed)**")
                            st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), use_container_width=True)
                        successful += 1
                        continue

                    face = faces[0]
                    angle = self.get_face_orientation(face)
                    
                    if angle in [90, 180, 270]:
                        image = self.rotate_image(image, angle)
                    
                    # Display processed image
                    with col2:
                        st.markdown(f"**Processed Image (Rotation: {angle}°)**")
                        st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), use_container_width=True)

                    cv2.imwrite(output_path, image)
                    successful += 1
                    
                except Exception as e:
                    st.error(f"Error processing {input_path}: {str(e)}")
                    failed += 1

                # Update progress
                progress = (idx + 1) / len(image_files)
                progress_bar.progress(progress)
                status_text.text(f"Processing: {idx + 1}/{len(image_files)} images")

        st.markdown("---")  # Final horizontal rule
        st.success(f"Processing completed: Total: {len(image_files)}, Successful: {successful}, Failed: {failed}")

class ImageMatcher:
    def __init__(self):
        self.face_analyzer = self._initialize_face_analyzer()
        self.cache = {}
    
    def _initialize_face_analyzer(self):
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            app = FaceAnalysis(
                name='buffalo_l',
                providers=['CPUExecutionProvider']
            )
            app.prepare(ctx_id=-1, det_size=(640, 640))
            return app

    def get_face_embedding(self, image_path):
        """Extract face embedding from image"""
        if image_path in self.cache:
            return self.cache[image_path]
        
        try:
            image = cv2.imread(image_path)
            if image is None:
                return None
            
            # Get face embeddings
            faces = self.face_analyzer.get(image)
            if not faces:
                return None
            
            # Get the largest face (assuming it's the main subject)
            largest_face = max(faces, key=lambda x: x.bbox[2] * x.bbox[3])
            embedding = largest_face.embedding
            
            # Cache the result
            self.cache[image_path] = embedding
            return embedding
            
        except Exception as e:
            logging.warning(f"Error getting face embedding for {image_path}: {str(e)}")
            return None

    def compare_faces(self, emb1, emb2, threshold=0.4):
        """Compare two face embeddings using cosine similarity"""
        if emb1 is None or emb2 is None:
            return False
        
        # Calculate cosine similarity
        similarity = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
        return similarity > threshold

    def find_matching_images(self, processed_thumbnails_folder, originals_folder, output_folder, threshold=0.4):
        if not os.path.exists(output_folder):
            os.makedirs(output_folder)

        # Process original images
        original_files = [f for f in os.listdir(originals_folder) 
                        if f.lower().endswith(('.jpg', '.jpeg', '.png'))]
        
        # Create containers
        progress_container = st.container()
        metrics_container = st.container()
        image_container = st.container()
        
        with progress_container:
            progress_bar = st.progress(0)
            status_text = st.empty()
        
        with metrics_container:
            col1, col2, col3, col4 = st.columns(4)
            total_processed = col1.empty()
            matches_found = col2.empty()
            current_similarity = col3.empty()
            best_similarity_metric = col4.empty()
        
        # Calculate embeddings for original images
        original_embeddings = {}
        status_text.text("Processing original images...")
        
        for idx, original_file in enumerate(original_files):
            original_path = os.path.join(originals_folder, original_file)
            embedding = self.get_face_embedding(original_path)
            if embedding is not None:
                original_embeddings[original_path] = embedding
            progress_bar.progress((idx + 1) / len(original_files))

        # Process thumbnails and find matches
        thumbnail_files = [f for f in os.listdir(processed_thumbnails_folder) 
                        if f.lower().endswith(('.jpg', '.jpeg', '.png'))]
        copied_files = set()
        total_matches = 0

        status_text.text("Matching images...")
        
        for idx, thumbnail_file in enumerate(thumbnail_files):
            with image_container:
                st.markdown("---")  # Horizontal rule
                st.markdown(f"**Comparing {idx + 1}/{len(thumbnail_files)}: {thumbnail_file}**")
                
                col1, col2 = st.columns(2)
                
                thumbnail_path = os.path.join(processed_thumbnails_folder, thumbnail_file)
                thumbnail_embedding = self.get_face_embedding(thumbnail_path)
                
                total_processed.metric("Processed", f"{idx + 1}/{len(thumbnail_files)}")
                matches_found.metric("Matches", total_matches)
                
                if thumbnail_embedding is None:
                    continue

                # Display thumbnail
                with col1:
                    st.markdown("**Current Thumbnail**")
                    st.image(PILImage.open(thumbnail_path), use_container_width=True)

                best_match = None
                best_similarity = -1

                for original_path, original_embedding in original_embeddings.items():
                    similarity = np.dot(thumbnail_embedding, original_embedding) / (
                        np.linalg.norm(thumbnail_embedding) * np.linalg.norm(original_embedding)
                    )
                    
                    current_similarity.metric("Current Similarity", f"{similarity:.3f}")
                    
                    if similarity > best_similarity and similarity > threshold:
                        best_similarity = similarity
                        best_match = original_path
                        best_similarity_metric.metric("Best Similarity", f"{best_similarity:.3f}")

                if best_match:
                    # Display match
                    with col2:
                        st.markdown("**Best Match**")
                        st.image(PILImage.open(best_match), use_container_width=True)
                        st.markdown(f"*Similarity: {best_similarity:.3f}*")

                    if os.path.basename(best_match) not in copied_files:
                        output_path = os.path.join(output_folder, os.path.basename(best_match))
                        shutil.copy2(best_match, output_path)
                        copied_files.add(os.path.basename(best_match))
                        total_matches += 1
                        matches_found.metric("Matches", total_matches)

                progress_bar.progress((idx + 1) / len(thumbnail_files))

        st.markdown("---")  # Final horizontal rule
        st.success(f"Image matching completed. Total matches found: {total_matches}")

def validate_paths(thumbnails_folder, processed_thumbnails_folder, originals_folder, output_folder):
    """Validate all input paths"""
    errors = []
    
    # Check if paths are not empty
    if not thumbnails_folder:
        errors.append("Thumbnails folder path cannot be empty")
    if not processed_thumbnails_folder:
        errors.append("Processed thumbnails folder path cannot be empty")
    if not originals_folder:
        errors.append("Originals folder path cannot be empty")
    if not output_folder:
        errors.append("Output folder path cannot be empty")
    
    # Check if input folders exist
    if thumbnails_folder and not os.path.exists(thumbnails_folder):
        errors.append(f"Thumbnails folder does not exist: {thumbnails_folder}")
    if originals_folder and not os.path.exists(originals_folder):
        errors.append(f"Originals folder does not exist: {originals_folder}")
    
    return errors

def folder_selector(label, help_text):
    """Create a folder selector with browse button"""
    col1, col2 = st.columns([3, 1])
    
    # Store the folder path in session state if not already present
    if f'folder_{label}' not in st.session_state:
        st.session_state[f'folder_{label}'] = ""

    # Text input for the folder path
    folder_path = col1.text_input(
        label,
        value=st.session_state[f'folder_{label}'],
        help=help_text,
        key=f"input_{label}"
    )

    # Browse button
    if col2.button("Browse", key=f"browse_{label}"):
        try:
            import tkinter as tk
            from tkinter import filedialog
            
            root = tk.Tk()
            root.withdraw()
            root.wm_attributes('-topmost', 1)
            
            # Create and handle dialog in a separate thread
            selected_folder = filedialog.askdirectory(title=f"Select {label}")
            
            if selected_folder:
                st.session_state[f'folder_{label}'] = selected_folder
                folder_path = selected_folder
                
            root.destroy()
            
            st.rerun()
            
        except Exception as e:
            st.error(f"Error selecting folder: {str(e)}")
    
    return folder_path

def is_folder_empty(folder_path):
    """Check if a folder is empty"""
    try:
        return not any(
            f for f in os.scandir(folder_path) 
            if f.name.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.tiff'))
        )
    except Exception as e:
        st.error(f"Error checking folder {folder_path}: {str(e)}")
        return True

def main():
    st.set_page_config(page_title="Photo Matching Assistant", layout="wide")
    st.title("Photo Matching Assistant")

    # Main content area for folder configuration
    st.header("Folder Configuration")
    
    # Create two columns for better layout
    col1, col2 = st.columns(2)
    
    with col1:
        # Source and Processed folders
        source_folder = folder_selector(
            "Source Photos (Small)",
            "Folder containing the smaller version of photos to process"
        )
        
        processed_folder = folder_selector(
            "Processed Photos",
            "Folder where processed photos will be saved"
        )
    
    with col2:
        # Reference and Selected folders
        reference_folder = folder_selector(
            "Reference Photos (Original)",
            "Folder containing the original high-resolution photos"
        )
        
        selected_folder = folder_selector(
            "Selected Photos",
            "Folder where selected photos will be saved"
        )

    # Sidebar for matching configuration and status
    st.sidebar.header("Matching Configuration")
    threshold = st.sidebar.slider(
        "Face Matching Sensitivity",
        min_value=0.3,
        max_value=0.7,
        value=0.4,
        step=0.01,
        help="Adjust how strictly photos should match (higher = more strict)"
    )

    # Main content tabs
    st.markdown("---")  # Horizontal line for separation
    tab1, tab2 = st.tabs(["Photo Orientation", "Photo Matching"])

    with tab1:
        st.header("Photo Orientation Processing")
        st.markdown("""
        This step processes your source photos and corrects their orientation based on face detection.
        The processed photos will be saved in the processed folder.
        """)
        
        if st.button("Start Orientation Processing", key="start_orientation"):
            # Check if required folders are selected
            if not all([source_folder, processed_folder]):
                st.error("Please select both source and processed folders")
                return
            
            # Check if source folder exists and is not empty
            if not os.path.exists(source_folder):
                st.error("Source Photos folder does not exist. Please select a valid folder.")
                return
            
            if is_folder_empty(source_folder):
                st.error("Source Photos folder is empty. Please select a folder containing images.")
                return
            
            # If all checks pass, proceed with orientation processing
            try:
                os.makedirs(processed_folder, exist_ok=True)
                processor = ImageProcessor(source_folder, processed_folder)
                processor.process_thumbnails()
            except Exception as e:
                st.error(f"An error occurred during orientation processing: {str(e)}")

    with tab2:
        st.header("Photo Matching")
        st.markdown("""
        This step matches the processed photos with your original high-resolution photos
        and saves the matched photos in the output folder.
        """)
        
        if st.button("Start Photo Matching", key="start_matching"):
            # Check if required folders are selected
            if not all([processed_folder, reference_folder, selected_folder]):
                st.error("Please select all required folders")
                return
            
            # Check if processed folder exists and is not empty
            if not os.path.exists(processed_folder):
                st.error("Processed Photos folder does not exist. Please complete the Photo Orientation step first.")
                return
            
            if is_folder_empty(processed_folder):
                st.error("Processed Photos folder is empty. Please complete the Photo Orientation step first.")
                return
            
            # If all checks pass, proceed with matching
            try:
                os.makedirs(selected_folder, exist_ok=True)
                matcher = ImageMatcher()
                matcher.find_matching_images(
                    processed_folder,
                    reference_folder,
                    selected_folder,
                    threshold=threshold
                )
            except Exception as e:
                st.error(f"An error occurred during photo matching: {str(e)}")

    # Status section in sidebar
    st.sidebar.markdown("---")
    st.sidebar.markdown("### Processing Status")
    
    if processed_folder and os.path.exists(processed_folder):
        processed_count = len([f for f in os.listdir(processed_folder) 
                             if f.lower().endswith(('.jpg', '.jpeg', '.png'))])
        st.sidebar.info(f"Processed photos: {processed_count}")
    
    if selected_folder and os.path.exists(selected_folder):
        selected_count = len([f for f in os.listdir(selected_folder) 
                            if f.lower().endswith(('.jpg', '.jpeg', '.png'))])
        st.sidebar.info(f"Selected photos: {selected_count}")

if __name__ == "__main__":
    main()