app.py

import os
import zipfile
import torch
import clip
import numpy as np
from PIL import Image
import gradio as gr
import openai
from tqdm import tqdm
from glob import glob
import chromadb
from chromadb.utils import embedding_functions
import json
import time
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# ─────────────────────────────────────────────
# 📂 STEP 1: UNZIP TO CORRECT STRUCTURE
# ─────────────────────────────────────────────
zip_name = "lfw-faces.zip"
unzip_dir = "lfw-faces"

if not os.path.exists(unzip_dir):
    print("🔓 Unzipping...")
    with zipfile.ZipFile(zip_name, "r") as zip_ref:
        zip_ref.extractall(unzip_dir)
    print("✅ Unzipped into:", unzip_dir)

# True image root after unzip
img_root = os.path.join(unzip_dir, "lfw-deepfunneled")

# ─────────────────────────────────────────────
# 🧠 STEP 2: LOAD CLIP MODEL
# ─────────────────────────────────────────────
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)
print(f"✅ CLIP model loaded on {device}")

# ─────────────────────────────────────────────
# 🗄️ STEP 3: CHROMA DB SETUP & EMBEDDING FUNCTION
# ─────────────────────────────────────────────
class ClipEmbeddingFunction:
    """Custom embedding function for Chroma DB using CLIP"""
    
    def __init__(self, model, preprocess, device):
        self.model = model
        self.preprocess = preprocess
        self.device = device
    
    def __call__(self, images):
        """Generate embeddings for a list of image paths"""
        embeddings = []
        
        for image_path in images:
            try:
                # Check if the path is a string (for new additions from disk)
                if isinstance(image_path, str) and os.path.exists(image_path):
                    img = Image.open(image_path).convert("RGB")
                else:
                    # For query images that are already PIL images
                    img = image_path.convert("RGB") if hasattr(image_path, 'convert') else image_path
                
                img_input = self.preprocess(img).unsqueeze(0).to(self.device)
                with torch.no_grad():
                    emb = self.model.encode_image(img_input).cpu().numpy().flatten()
                    emb /= np.linalg.norm(emb)
                embeddings.append(emb.tolist())
            except Exception as e:
                print(f"⚠️ Error embedding image: {e}")
                # Return a zero vector as fallback
                embeddings.append([0] * 512)
        
        return embeddings

def setup_database():
    """Setup ChromaDB with CLIP embedding function"""
    try:
        # Create persistent client
        client = chromadb.PersistentClient(path="./chroma_db")
        
        # Create custom embedding function
        embedding_function = ClipEmbeddingFunction(model, preprocess, device)
        
        # Create or get existing collection
        collection = client.get_or_create_collection(
            name="faces",
            embedding_function=embedding_function,
            metadata={"hnsw:space": "cosine"}  # Use cosine similarity
        )
        
        print("✅ ChromaDB setup complete.")
        return client, collection
    except Exception as e:
        print(f"❌ Database setup failed: {e}")
        return None, None

def populate_database(collection, limit=500):
    """Populate ChromaDB with images and their embeddings"""
    # Collect all .jpg files inside subfolders
    all_images = sorted(glob(os.path.join(img_root, "*", "*.jpg")))
    selected_images = all_images[:limit]
    
    if len(selected_images) == 0:
        raise RuntimeError("❌ No image files found in unzipped structure!")
    
    # Get existing IDs
    existing_ids = set()
    try:
        existing_count = collection.count()
        if existing_count > 0:
            results = collection.get(limit=existing_count)
            existing_ids = set(results['ids'])
    except Exception as e:
        print(f"Error getting existing IDs: {e}")
    
    # Filter out images that are already in the database
    new_images = []
    new_ids = []
    new_metadatas = []
    
    for fpath in selected_images:
        # Create ID from path
        image_id = fpath.replace('/', '_')
        if image_id not in existing_ids:
            new_images.append(fpath)
            new_ids.append(image_id)
            name = os.path.splitext(os.path.basename(fpath))[0].replace("_", " ")
            new_metadatas.append({
                "path": fpath,
                "name": name
            })
    
    if not new_images:
        print("✅ All images are already in the database.")
        return
    
    print(f"🧠 Adding {len(new_images)} new images to the database...")
    
    # Process images in batches to avoid memory issues
    batch_size = 50
    for i in range(0, len(new_images), batch_size):
        batch_imgs = new_images[i:i+batch_size]
        batch_ids = new_ids[i:i+batch_size]
        batch_metadatas = new_metadatas[i:i+batch_size]
        
        print(f"Processing batch {i//batch_size + 1}/{(len(new_images)-1)//batch_size + 1}...")
        
        try:
            collection.add(
                documents=batch_imgs,  # ChromaDB will call our embedding function on these
                ids=batch_ids,
                metadatas=batch_metadatas
            )
        except Exception as e:
            print(f"⚠️ Error adding batch to database: {e}")
    
    # Count total faces in database
    total_faces = collection.count()
    print(f"✅ Database now contains {total_faces} faces.")

# ─────────────────────────────────────────────
# 🔐 STEP 4: LOAD OPENAI API KEY
# ─────────────────────────────────────────────
openai.api_key = os.getenv("OPENAI_API_KEY")
if not openai.api_key:
    print("⚠️ OpenAI API key not found. GPT-4 analysis will not work.")

# ─────────────────────────────────────────────
# 🔍 STEP 5: FACE MATCHING FUNCTION
# ─────────────────────────────────────────────
def scan_face(user_image, collection):
    """Scan a face image and find matches in the database"""
    if user_image is None:
        return [], "", "", "Please upload a face image."

    try:
        # Query database for similar faces using the image directly
        results = collection.query(
            query_embeddings=None,  # Will be generated by our embedding function
            query_images=[user_image],  # Pass the PIL image directly
            n_results=5,
            include=["metadatas", "distances"]
        )
        
        metadatas = results.get("metadatas", [[]])[0]
        distances = results.get("distances", [[]])[0]
        
        gallery, captions, names = [], [], []
        scores = []
        
        for i, metadata in enumerate(metadatas):
            try:
                path = metadata["path"]
                name = metadata["name"]
                
                # Convert distance to similarity score (1 - normalized_distance)
                # ChromaDB uses cosine distance, so 0 is most similar, 2 is most different
                distance = distances[i]
                similarity = 1 - (distance / 2)  # Convert to 0-1 scale
                scores.append(similarity)
                
                img = Image.open(path)
                gallery.append(img)
                captions.append(f"{name} (Score: {similarity:.2f})")
                names.append(name)
            except Exception as e:
                captions.append(f"⚠️ Error loading match image: {e}")
        
        risk_score = min(100, int(np.mean(scores) * 100)) if scores else 0

        # 🧠 GPT-4 EXPLANATION
        explanation = ""
        if openai.api_key and names:
            try:
                prompt = (
                    f"The uploaded face matches closely with: {', '.join(names)}. "
                    f"Based on this, should the user be suspicious? Analyze like a funny but smart AI dating detective."
                )
                response = openai.chat.completions.create(
                    model="gpt-4",
                    messages=[
                        {"role": "system", "content": "You're a playful but intelligent AI face-matching analyst."},
                        {"role": "user", "content": prompt}
                    ]
                )
                explanation = response.choices[0].message.content
            except Exception as e:
                explanation = f"(OpenAI error): {e}"
        else:
            explanation = "OpenAI API key not set or no matches found."

        return gallery, "\n".join(captions), f"{risk_score}/100", explanation
    
    except Exception as e:
        return [], "", "", f"Error scanning face: {e}"

# ─────────────────────────────────────────────
# 🌱 STEP 6: ADD NEW FACE FUNCTION
# ─────────────────────────────────────────────
def add_new_face(image, name, collection):
    """Add a new face to the database"""
    if image is None or not name:
        return "Please provide both an image and a name."
    
    try:
        # Save image to a temporary file
        timestamp = int(time.time())
        os.makedirs("uploaded_faces", exist_ok=True)
        path = f"uploaded_faces/{name.replace(' ', '_')}_{timestamp}.jpg"
        image.save(path)
        
        # Add to ChromaDB
        image_id = path.replace('/', '_')
        collection.add(
            documents=[path],
            ids=[image_id],
            metadatas=[{
                "path": path,
                "name": name
            }]
        )
        
        return f"✅ Added {name} to the database successfully!"
    except Exception as e:
        return f"❌ Failed to add face: {e}"

# ─────────────────────────────────────────────
# 🎛️ STEP 7: GRADIO UI
# ─────────────────────────────────────────────
def create_ui():
    """Create Gradio UI with both scan and add functionality"""
    # Setup database
    client, collection = setup_database()
    if collection is None:
        raise RuntimeError("❌ Database setup failed.")
    
    # Populate database with initial images
    populate_database(collection)
    
    # Wrapper functions for Gradio that use the database collection
    def scan_face_wrapper(image):
        return scan_face(image, collection)
    
    def add_face_wrapper(image, name):
        return add_new_face(image, name, collection)
    
    with gr.Blocks(title="Tinder Scanner – Real Face Match Detector") as demo:
        gr.Markdown("# Tinder Scanner – Real Face Match Detector")
        gr.Markdown("Scan a face image to find visual matches using CLIP and ChromaDB, and get a cheeky GPT-4 analysis.")
        
        with gr.Tab("Scan Face"):
            with gr.Row():
                with gr.Column():
                    input_image = gr.Image(type="pil", label="Upload a Face Image")
                    scan_button = gr.Button("🔍 Scan Face")
                
                with gr.Column():
                    gallery = gr.Gallery(label="🔍 Top Matches", columns=[5], height="auto")
                    captions = gr.Textbox(label="Match Names + Similarity Scores")
                    risk_score = gr.Textbox(label="🚨 Cheating Risk Score")
                    explanation = gr.Textbox(label="🧠 GPT-4 Explanation", lines=5)
            
            scan_button.click(
                fn=scan_face_wrapper,
                inputs=[input_image],
                outputs=[gallery, captions, risk_score, explanation]
            )
        
        with gr.Tab("Add New Face"):
            with gr.Row():
                with gr.Column():
                    new_image = gr.Image(type="pil", label="Upload New Face Image")
                    new_name = gr.Textbox(label="Person's Name")
                    add_button = gr.Button("➕ Add to Database")
                
                with gr.Column():
                    result = gr.Textbox(label="Result")
            
            add_button.click(
                fn=add_face_wrapper,
                inputs=[new_image, new_name],
                outputs=result
            )
    
    return demo

# ─────────────────────────────────────────────
# 🚀 MAIN EXECUTION
# ─────────────────────────────────────────────
if __name__ == "__main__":
    demo = create_ui()
    demo.launch()