#!/usr/bin/env python3
"""
Automated Reverse Image Search using Google without API key and without Selenium.
Followed by sequential face recognition and matching.
"""

import os
import io
import time
import requests
import tempfile
import asyncio
import aiohttp
import numpy as np
import gradio as gr
from PIL import Image
from urllib.parse import urlparse
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import cosine_similarity

# Suppress TensorFlow logging unless in debug mode
import logging
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # Suppress TensorFlow INFO and WARNING messages
logging.getLogger('tensorflow').setLevel(logging.ERROR)

# TensorFlow imports
from tensorflow.keras.applications import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array

# Face Recognition imports
import face_recognition

from loguru import logger

# PicImageSearch imports
from PicImageSearch import Bing, Tineye, Network
from PicImageSearch.model import BingResponse, TineyeResponse

# ----------------------------------------
# 1. Utility Functions
# ----------------------------------------

def show_progress_bar(current, total, step_name="Processing", width=50):
    """Shows a progress bar for the current step"""
    if total == 0:
        return
    
    percent = current / total
    filled = int(width * percent)
    bar = '█' * filled + '░' * (width - filled)
    
    # Clear line and show progress
    print(f'\r{step_name}: [{bar}] {current}/{total} ({percent:.1%})', end='', flush=True)
    
    if current == total:
        print()  # New line when complete

def animate_step(step_name, duration=1.0):
    """Shows an animated loading indicator for a step"""
    chars = "⠋⠙⠹⠸⠼⠴⠦⠧⠇⠏"
    end_time = time.time() + duration
    i = 0
    
    while time.time() < end_time:
        print(f'\r{chars[i % len(chars)]} {step_name}...', end='', flush=True)
        time.sleep(0.1)
        i += 1
    
    print(f'\r✅ {step_name} completed!           ')

# ----------------------------------------
# 2. Face Recognition and VGG16-based Feature Extraction
# ----------------------------------------

def get_face_encoding_from_image(image_path, resize_max=800):
    """
    Detects the first face in an image file and returns the 128D embedding, or None.
    """
    try:
        # Load image using face_recognition
        img = face_recognition.load_image_file(image_path)
        
        # Get face locations and encodings
        locations = face_recognition.face_locations(img, model='cnn')
        if not locations:
            return None
        
        encodings = face_recognition.face_encodings(img, locations)
        if not encodings:
            return None
        
        return encodings[0]
    except Exception:
        return None

def get_face_encoding_from_pil_image(pil_image):
    """
    Detects the first face in a PIL image and returns the 128D embedding, or None.
    """
    try:
        # Convert PIL image to numpy array for face_recognition
        img_array = np.array(pil_image)
        
        # Get face locations and encodings
        locations = face_recognition.face_locations(img_array)
        if not locations:
            return None
        
        encodings = face_recognition.face_encodings(img_array, locations)
        if not encodings:
            return None
        
        return encodings[0]
    except Exception:
        return None

# Global VGG16 model
_vgg16_model = None

def get_vgg16_model(debug=False):
    """Loads the VGG16 model once"""
    global _vgg16_model
    if _vgg16_model is None:
        if debug:
            print("Loading VGG16 model...")
        _vgg16_model = VGG16(weights="imagenet", include_top=False, pooling="avg")
        if debug:
            print("VGG16 model loaded!")
    return _vgg16_model

def extract_vgg16_features(image: Image.Image, target_size=(224, 224), debug=False):
    """
    Extracts VGG16 features from a PIL image
    """
    try:
        # Resize image to VGG16 input size
        img = image.resize(target_size, Image.LANCZOS)
        img_array = img_to_array(img)
        img_array = np.expand_dims(img_array, axis=0)
        img_array = preprocess_input(img_array)
        
        # Extract features
        model = get_vgg16_model(debug)
        features = model.predict(img_array, verbose=0)
        return features.flatten()
    except Exception as e:
        if debug:
            print(f"Error in feature extraction: {e}")
        return None

def get_query_features(input_image_path, debug=False):
    """
    Loads the input image and extracts VGG16 features
    """
    if not os.path.exists(input_image_path):
        if debug:
            print(f"[get_query_features] Input image not found: {input_image_path}")
        return None
    try:
        img = Image.open(input_image_path).convert('RGB')
        features = extract_vgg16_features(img, debug=debug)
        img.close()
        if features is None and debug:
            print("Error in feature extraction from input image.")
        return features
    except Exception as e:
        if debug:
            print(f"[get_query_features] Error loading: {e}")
        return None

# ----------------------------------------
# 3. PicImageSearch-based Reverse Image Search
# ----------------------------------------

def extract_urls_from_bing_response(resp: BingResponse) -> list:
    """Extracts image URLs and their source page URLs from Bing response"""
    url_pairs = []  # List of (image_url, source_page_url) tuples
    
    # Pages including
    if resp.pages_including:
        for item in resp.pages_including:
            image_url = None
            source_url = None
            
            # Get image URL (prefer original over thumbnail)
            if hasattr(item, 'image_url') and item.image_url:
                image_url = item.image_url
            elif hasattr(item, 'thumbnail') and item.thumbnail:
                image_url = item.thumbnail
            
            # Get source page URL
            if hasattr(item, 'url') and item.url:
                source_url = item.url
            elif hasattr(item, 'source') and item.source:
                source_url = item.source
            
            if image_url and source_url:
                url_pairs.append((image_url, source_url))
    
    # Visual search
    if resp.visual_search:
        for item in resp.visual_search:
            image_url = None
            source_url = None
            
            # Get image URL (prefer original over thumbnail)
            if hasattr(item, 'image_url') and item.image_url:
                image_url = item.image_url
            elif hasattr(item, 'thumbnail') and item.thumbnail:
                image_url = item.thumbnail
            
            # Get source page URL
            if hasattr(item, 'url') and item.url:
                source_url = item.url
            elif hasattr(item, 'source') and item.source:
                source_url = item.source
            
            if image_url and source_url:
                url_pairs.append((image_url, source_url))
                
    return url_pairs

async def bing_reverse_image_search(image_path, max_results=100, debug=False):
    """
    Performs Bing Reverse Image Search with PicImageSearch
    """
    if debug:
        print(f"[BING-PIC] === Starting Bing Reverse Search ===")
        print(f"[BING-PIC] Image Path: {image_path}")
        print(f"[BING-PIC] Max Results: {max_results}")
    
    try:
        async with Network(proxies=None) as client:
            bing = Bing(client=client)
            resp = await bing.search(file=image_path)
            
            if debug:
                print(f"[BING-PIC] Search URL: {resp.url}")
            
            url_pairs = extract_urls_from_bing_response(resp)
            if debug:
                print(f"[BING-PIC] Found URL pairs: {len(url_pairs)}")
            
            return url_pairs[:max_results]
            
    except Exception as e:
        print(f"[BING-PIC] ✗ Error: {e}")
        logger.exception("Error in bing_reverse_image_search:")
        return []

async def bing_with_tineye_fallback_search(image_path, max_results=200, debug=False):
    """
    Performs Bing Reverse Search first, then TinEye as fallback if no results
    """
    if debug:
        print(f"[SEARCH] === Starting Reverse Image Search ===")
        print(f"[SEARCH] Strategy: Bing first, TinEye fallback")
    
    # Try Bing Search first
    if debug:
        print("\n1. Starting Bing Search...")
    bing_start = time.time()
    bing_url_pairs = await bing_reverse_image_search(image_path, max_results, debug)
    bing_time = time.time() - bing_start
    if debug:
        print(f"[SEARCH] Bing: {len(bing_url_pairs)} URL pairs in {bing_time:.2f}s")
    
    # If Bing found results, use them
    if bing_url_pairs:
        if debug:
            print(f"[SEARCH] ✅ Bing found {len(bing_url_pairs)} results, using Bing results")
            print(f"[SEARCH] === Search Completed (Bing) ===")
        return bing_url_pairs
    
    # If Bing found nothing, try TinEye as fallback
    if debug:
        print(f"[SEARCH] ⚠️ Bing found no results, trying TinEye as fallback...")
        print("\n2. Starting TinEye Search...")
    
    tineye_start = time.time()
    tineye_url_pairs = await tineye_reverse_image_search(image_path, max_results, debug)
    tineye_time = time.time() - tineye_start
    if debug:
        print(f"[SEARCH] TinEye: {len(tineye_url_pairs)} URL pairs in {tineye_time:.2f}s")
    
    if tineye_url_pairs:
        if debug:
            print(f"[SEARCH] ✅ TinEye found {len(tineye_url_pairs)} results, using TinEye results")
            print(f"[SEARCH] === Search Completed (TinEye Fallback) ===")
        return tineye_url_pairs
    else:
        if debug:
            print(f"[SEARCH] ❌ Both Bing and TinEye found no results")
            print(f"[SEARCH] === Search Completed (No Results) ===")
        return []

def extract_urls_from_tineye_response(resp: TineyeResponse) -> list:
    """Extracts image URLs and their source page URLs from TinEye response"""
    url_pairs = []  # List of (image_url, source_page_url) tuples
    
    if resp and resp.raw:
        for item in resp.raw:
            image_url = None
            source_url = None
            
            # Get image URL
            if hasattr(item, 'image_url') and item.image_url:
                image_url = item.image_url
            elif hasattr(item, 'thumbnail') and item.thumbnail:
                image_url = item.thumbnail
            
            # Get source page URL (backlink)
            if hasattr(item, 'url') and item.url:
                source_url = item.url
                
            if image_url and source_url:
                url_pairs.append((image_url, source_url))
                
    return url_pairs

async def tineye_reverse_image_search(image_path, max_results=100, debug=False):
    """
    Performs TinEye Reverse Image Search with PicImageSearch
    """
    if debug:
        print(f"[TINEYE] === Starting TinEye Reverse Search ===")
        print(f"[TINEYE] Image Path: {image_path}")
        print(f"[TINEYE] Max Results: {max_results}")
    
    try:
        async with Network(proxies=None) as client:
            tineye = Tineye(client=client)
            resp = await tineye.search(
                file=image_path,
                show_unavailable_domains=False,
                domain="",
                tags="",
                sort="score",
                order="desc",
            )
            
            if debug:
                if resp and hasattr(resp, 'query_hash'):
                    print(f"[TINEYE] Query Hash: {resp.query_hash}")
                if resp and hasattr(resp, 'total_pages'):
                    print(f"[TINEYE] Total Pages: {resp.total_pages}")
            
            url_pairs = extract_urls_from_tineye_response(resp)
            if debug:
                print(f"[TINEYE] Found URL pairs: {len(url_pairs)}")
            
            return url_pairs[:max_results]
            
    except Exception as e:
        print(f"[TINEYE] ✗ Error: {e}")
        logger.exception("Error in tineye_reverse_image_search:")
        return []

# ----------------------------------------
# 4. Two-stage processing: VGG16 + Face-Recognition
# ----------------------------------------
async def process_candidate_urls_two_stage(url_pairs, query_features, query_face_encoding, vgg16_threshold=0.85, top_k=20, debug=False):
    """
    Two-stage approach:
    1. VGG16-Filter: REMOVE images with >85% general similarity (too similar/identical)
    2. Face-Recognition: Sort ALL filtered images by face similarity
    
    Returns list [(face_similarity, source_page_url), ...] sorted (descending face similarity).
    """
    stage1_candidates = []
    stage2_matches = []
    timeout = aiohttp.ClientTimeout(total=10)
    connector = aiohttp.TCPConnector(limit_per_host=5)
    
    if debug:
        print(f"=== TWO-STAGE FILTER ===")
        print(f"Stage 1: VGG16-Filter (REMOVES images with >{vgg16_threshold*100:.0f}% image similarity)")
        print(f"Stage 2: Face-Recognition (ALL remaining faces sorted by similarity)")
        print(f"Processing {len(url_pairs)} URL pairs...")
    
    async with aiohttp.ClientSession(timeout=timeout, connector=connector) as session:        # STAGE 1: VGG16-Filter (REVERSE - removes too similar images)
        if debug:
            print(f"\n--- STAGE 1: VGG16-Filter (removes too similar images) ---")
        
        total_pairs = len(url_pairs)
        for i, (image_url, source_page_url) in enumerate(url_pairs):
            if not debug:
                show_progress_bar(i + 1, total_pairs, "Stage 1: VGG16 filtering")
                
            try:
                async with session.get(image_url) as resp:
                    if resp.status != 200:
                        continue
                    ctype = resp.headers.get("Content-Type", "")
                    if "image" not in ctype:
                        continue
                    data = await resp.read()
                    img = Image.open(io.BytesIO(data)).convert('RGB')
            except Exception:
                continue            # Extract VGG16 features
            features = extract_vgg16_features(img, debug=debug)
            if features is None:
                img.close()
                continue

            # Calculate VGG16 similarity
            try:
                vgg16_similarity = cosine_similarity([query_features], [features])[0][0]
            except Exception:
                img.close()
                continue
              # Stage 1 Filter: Only images UNDER VGG16 threshold (less similar)
            if vgg16_similarity < vgg16_threshold:
                stage1_candidates.append((vgg16_similarity, image_url, source_page_url, img))
                if debug and len(stage1_candidates) % 10 == 0:
                    print(f"  Stage 1: {len(stage1_candidates)} candidates kept (from {i+1} processed URL pairs)")
            else:
                img.close()
                # These images are filtered out (too similar)            # Monitoring
            if debug and (i + 1) % 50 == 0:
                print(f"  Processed {i+1}/{len(url_pairs)} URL pairs, kept: {len(stage1_candidates)} (removed too similar)")
        
        if debug:
            print(f"✅ Stage 1 completed: {len(stage1_candidates)} candidates under {vgg16_threshold*100:.0f}% VGG16 similarity kept")
        
        if not stage1_candidates:
            if debug:
                print("❌ All images were too similar to original (above VGG16 threshold)!")
            return []

        # ADDITIONAL STEP: VGG16 duplicate filtering between candidates
        if debug:
            print(f"\n--- ADDITIONAL: VGG16 duplicate filtering between candidates ---")
        elif not debug:
            animate_step("Removing duplicates", 0.5)
            
        filtered_candidates = []
        candidate_features = []
        
        # Extract features of all candidates
        for i, (vgg16_sim, image_url, source_page_url, img) in enumerate(stage1_candidates):
            features = extract_vgg16_features(img, debug=debug)
            if features is not None:
                candidate_features.append(features)
                filtered_candidates.append((vgg16_sim, image_url, source_page_url, img, features))
        
        # Remove similar images among each other
        unique_candidates = []
        used_indices = set()
        
        for i, (vgg16_sim, image_url, source_page_url, img, features) in enumerate(filtered_candidates):
            if i in used_indices:
                continue
                
            is_unique = True
            for j, (_, _, _, _, other_features) in enumerate(filtered_candidates[:i]):
                if j in used_indices:
                    continue
                    
                try:
                    similarity = cosine_similarity([features], [other_features])[0][0]
                    if similarity >= vgg16_threshold:  # Too similar to an already selected image
                        is_unique = False
                        break
                except Exception:
                    continue
            
            if is_unique:
                unique_candidates.append((vgg16_sim, image_url, source_page_url, img))
            else:
                img.close()  # Close similar image
                used_indices.add(i)
        
        if debug:
            print(f"✅ Duplicate filtering: {len(stage1_candidates)} → {len(unique_candidates)} unique candidates")        # STAGE 2: Face-Recognition on ALL filtered candidates
        if debug:
            print(f"\n--- STAGE 2: Face-Recognition (compare all faces at once) ---")
            
        # Extract all face encodings first
        all_face_encodings = []
        valid_candidates = []
        
        total_candidates = len(unique_candidates)
        for i, (vgg16_sim, image_url, source_page_url, img) in enumerate(unique_candidates):
            if not debug:
                show_progress_bar(i + 1, total_candidates, "Stage 2: Face extraction")
                
            # Extract face encoding
            face_encoding = get_face_encoding_from_pil_image(img)
            img.close()
            
            if face_encoding is not None:
                all_face_encodings.append(face_encoding)
                valid_candidates.append((vgg16_sim, image_url, source_page_url))
            # Monitoring
            if debug and (i + 1) % 10 == 0:
                print(f"  Face extraction: {i+1}/{len(unique_candidates)} processed, faces found: {len(all_face_encodings)}")

        if not all_face_encodings:
            if debug:
                print("❌ No faces found in any candidate images!")
            return []

        # Compare ALL faces at once using the official face_recognition approach
        if debug:
            print(f"Comparing {len(all_face_encodings)} faces with query face...")
        
        try:
            # Use face_recognition.compare_faces() exactly like in your example
            results = face_recognition.compare_faces(all_face_encodings, query_face_encoding)
            
            # Also get distances for ranking the matches
            face_distances = face_recognition.face_distance(all_face_encodings, query_face_encoding)            # Collect only the matches (where results[i] == True)
            for i, (is_match, face_distance) in enumerate(zip(results, face_distances)):
                if is_match == True:  # Like: if results[0] == True: print("It's a picture of me!")
                    face_similarity = 1 - face_distance  # Convert distance to similarity
                    
                    # Filter out faces that are not similar (under 50% similarity)
                    if face_similarity < 0.5:
                        if debug:
                            print(f"  Filtering out face {i}: {face_similarity*100:.1f}% similarity (not similar)")
                        continue
                    
                    _, image_url, source_page_url = valid_candidates[i]
                    stage2_matches.append((face_similarity, image_url, source_page_url))
            
            if debug:
                matches_found = len(stage2_matches)
                total_faces = len(results)
                matches_before_filter = len([r for r in results if r == True])
                filtered_faces = matches_before_filter - matches_found
                print(f"✅ Face comparison completed: {matches_before_filter}/{total_faces} faces match, {filtered_faces} filtered out (>50% similarity), {matches_found} kept!")
                
        except Exception as e:
            if debug:
                print(f"❌ Error in face comparison: {e}")
            return []

        # Sort by face similarity (descending order)
        stage2_matches.sort(key=lambda x: x[0], reverse=True)
          # Remove duplicates (same source URLs)
        seen_urls = set()
        unique_matches = []
        for face_similarity, image_url, source_url in stage2_matches:
            if source_url not in seen_urls:
                seen_urls.add(source_url)
                unique_matches.append((face_similarity, image_url, source_url))
        
        if debug and len(stage2_matches) != len(unique_matches):
            print(f"  🔄 {len(stage2_matches) - len(unique_matches)} duplicates removed")
        
        # Limit to top-K
        if len(unique_matches) > top_k:
            unique_matches = unique_matches[:top_k]

        if debug:
            print(f"✅ Stage 2 completed: {len(unique_matches)} unique faces found and sorted")
            if unique_matches:
                best_face_sim = unique_matches[0][0] * 100
                worst_face_sim = unique_matches[-1][0] * 100
                print(f"  Best face similarity: {best_face_sim:.1f}%")
                print(f"  Worst face similarity: {worst_face_sim:.1f}%")
        
        return unique_matches

# ----------------------------------------
# 5. Display of top matches
# ----------------------------------------
def show_top_matches(top_matches, debug=False):
    """
    Downloads the top match URLs again and shows thumbnails with URLs.
    """
    if not top_matches:
        print("No matches to display.")
        return
    
    print(f"\n🖼️  Loading {len(top_matches)} images for thumbnail display...")
    cols = min(len(top_matches), 5)
    rows = (len(top_matches) + cols - 1)//cols
    plt.figure(figsize=(4*cols, 4*rows))    
    successful_images = 0
    failed_images = []  # Track failed image URLs  
    total_matches = len(top_matches)
    
    for i, (similarity, image_url, source_url) in enumerate(top_matches):
        if not debug:
            show_progress_bar(i + 1, total_matches, "Loading thumbnails")
        elif debug:
            print(f"  Loading image {i+1}/{len(top_matches)}: {image_url[:60]}...")
        try:
            resp = requests.get(image_url, timeout=10, headers={
                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
            })
            if resp.status_code != 200:
                if debug:
                    print(f"    ❌ HTTP {resp.status_code}")
                failed_images.append((similarity, image_url, source_url))
                continue
            
            img = Image.open(io.BytesIO(resp.content)).convert('RGB')
            if debug:
                print(f"    ✅ Successfully loaded ({img.size})")
            
        except requests.exceptions.Timeout:
            if debug:
                print(f"    ❌ Timeout while loading")
            failed_images.append((similarity, image_url, source_url))
            continue
        except requests.exceptions.RequestException as e:
            if debug:
                print(f"    ❌ Network error: {e}")
            failed_images.append((similarity, image_url, source_url))
            continue
        except Exception as e:
            if debug:
                print(f"    ❌ Image error: {e}")
            failed_images.append((similarity, image_url, source_url))
            continue
            
        img.thumbnail((200, 200), Image.LANCZOS)
        ax = plt.subplot(rows, cols, i+1)
        ax.imshow(img)
        ax.axis('off')
          # Show similarity and URL
        similarity_percent = similarity * 100
        # Shorten URL for better display (domain + path)
        parsed_url = urlparse(source_url)
        short_url = f"{parsed_url.netloc}"
        if parsed_url.path:
            path_parts = parsed_url.path.split('/')
            if len(path_parts) > 1:
                short_url += f"/.../{path_parts[-1]}"
        # Display text below the image
        ax.text(0.5, -0.15, f"{similarity_percent:.1f}%\n{short_url}", 
                transform=ax.transAxes, ha='center', va='top',
                fontsize=8, wrap=True)
        
        successful_images += 1
    
    print(f"\n✅ {successful_images}/{len(top_matches)} images successfully loaded")
    
    if successful_images > 0:
        plt.tight_layout()
        plt.show()
    else:
        print("❌ No images could be loaded!")
        if debug:
            print("Possible causes:")
            print("- Images are no longer available")
            print("- Servers block access")
            print("- Network problems")
        
        # Show URLs as text fallback when images can't be loaded
        print("\n📋 Showing results as text list since images couldn't be loaded:")
        print("=" * 80)
        for idx, (similarity, image_url, source_url) in enumerate(top_matches, 1):
            similarity_percent = similarity * 100
            print(f"\n🏆 MATCH #{idx}:")
            print(f"   Face similarity: {similarity_percent:.1f}%")
            print(f"   Source page: {source_url}")
            print(f"   Image URL: {image_url}")
            print(f"   {'-' * 60}")
        print("=" * 80)


# ----------------------------------------
# Gradio Web Interface for Hugging Face Spaces
# ----------------------------------------

def format_gallery_results(top_matches):
    """
    Format the results for Gradio Gallery output: list of [image, caption] pairs.
    """
    if not top_matches:
        # Return None for empty results to avoid Gallery processing errors
        return None
    gallery = []
    for idx, (face_similarity, image_url, source_url) in enumerate(top_matches, 1):
        face_similarity_percent = face_similarity * 100
        caption = f"#{idx} – {face_similarity_percent:.1f}% | {source_url}"
        gallery.append([image_url, caption])
    return gallery


async def face_search_gradio(image, max_results=100, top_k=5, vgg16_threshold=0.85, debug=False):
    """
    Main Gradio function: takes a PIL image, runs the search, returns results as gallery and status message.
    """

    with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
        image.save(tmp.name)
        input_path = tmp.name

    # Step 1: Extract features
    query_features = get_query_features(input_path, debug=debug)
    if query_features is None:
        # Return None and error message when VGG16 feature extraction fails
        return None, "❌ Error: Could not extract VGG16 features from input image."
    query_face_encoding = get_face_encoding_from_image(input_path)
    if query_face_encoding is None:
        # Return None and error message when no face detected
        return None, "❌ Error: No face detected in input image."

    # Step 2: Reverse Image Search
    url_pairs = await bing_with_tineye_fallback_search(input_path, max_results=max_results, debug=debug)
    if not url_pairs:
        # Return None and message when no URLs found
        return None, "⚠️ No images found via reverse image search (tried Bing and TinEye)."

    # Step 3: Two-stage matching
    top_matches = await process_candidate_urls_two_stage(
        url_pairs,
        query_features,
        query_face_encoding,
        vgg16_threshold=vgg16_threshold,
        top_k=top_k,
        debug=debug
    )

    # Step 4: Format results
    gallery = format_gallery_results(top_matches)
    
    # Additional error handling: if no matches found after processing
    if not gallery:
        return None, "⚠️ No matching faces found above similarity thresholds."
    
    return gallery, f"✅ Found {len(top_matches)} matching faces!"

def update_status_visibility(status_text):
    """Make status field visible only when there's a message"""
    if status_text and status_text.strip():
        return gr.update(value=status_text, visible=True)
    else:
        return gr.update(value="", visible=False)


def gradio_sync_wrapper(image):
    """
    Wrapper to run async Gradio function in sync context, with fixed parameters.
    """
    # Fixed values for the most important parameters
    max_results = 100
    top_k = 10
    vgg16_threshold = 0.85
    debug = False
    return asyncio.run(face_search_gradio(image, max_results, top_k, vgg16_threshold, debug))

def hide_status():
    """Hide status message at start of search."""
    return gr.update(visible=False, value="")

with gr.Blocks() as demo:
    gr.Markdown("""
    # Face Search Tool Demo
    <div style='margin-bottom: 1em;'>
    <b>Upload a photo to find similar faces on the web (Bing/TinEye + Face Recognition).</b><br>
    The top 10 results will be shown as a gallery with links. This might take a minute.
    </div>
    """)
    image_input = gr.Image(type="pil", label="Upload an image", show_label=True)
    submit_btn = gr.Button("🔍 Start Search", elem_id="search-btn")
    gallery = gr.Gallery(label="Top 10 Matches", columns=5, height="auto", show_label=True)
    
    # Status message below gallery (only shows when there's a message)
    status_msg = gr.Textbox(label="Status", interactive=False, visible=False, show_label=False)
    
    # Create a chain: first hide status, then run search, then show status if needed
    submit_btn.click(
        hide_status,
        inputs=[],
        outputs=[status_msg]
    ).then(
        gradio_sync_wrapper,
        inputs=[image_input],
        outputs=[gallery, status_msg]
    ).then(
        update_status_visibility,
        inputs=[status_msg],
        outputs=[status_msg]
    )

demo.launch(share=True, server_name="0.0.0.0")