app.py

import os
import uuid
import tempfile
import gradio as gr
from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance, PointStruct
from sentence_transformers import SentenceTransformer
from PIL import Image
import torch
import numpy as np

# --------------------------
# Qdrant Cloud Connection
# --------------------------
QDRANT_URL = "https://ff4da494-27b1-413c-ba58-d5ea14932fe1.europe-west3-0.gcp.cloud.qdrant.io"  
QDRANT_API_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3MiOiJtIn0.98XRKd7ZdDXSfYDl44zbZ_VZ5csnh4tz1JACP62KZds"                    
COLLECTION_NAME = "lost_and_found"

# CLIP model (text + image embeddings)
MODEL_NAME = "sentence-transformers/clip-ViT-B-32"
embedder = SentenceTransformer(MODEL_NAME)
VECTOR_SIZE = embedder.get_sentence_embedding_dimension()

# Qdrant Client (Cloud)
qclient = QdrantClient(
    url=QDRANT_URL,
    api_key=QDRANT_API_KEY
)

# Ensure collection exists
qclient.recreate_collection(
    collection_name=COLLECTION_NAME,
    vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
)

# --------------------------
# Helper Functions
# --------------------------
def embed_text(text: str):
    """Generate embedding for text"""
    return embedder.encode(text).tolist()

def embed_image(image: Image.Image):
    """Generate embedding for image"""
    img_tensor = embedder.encode(image, convert_to_tensor=True)
    return img_tensor.cpu().detach().numpy().tolist()

# --------------------------
# Core Functions
# --------------------------
def add_item(description, image):
    """Add a found item to Qdrant"""
    if not description and image is None:
        return "⚠️ Please provide description or image."

    vectors = []
    payload = {"description": description}

    if description:
        vectors = embed_text(description)

    if image:
        vectors = embed_image(image)
        # Save uploaded image
        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
            image.save(tmp.name)
            payload["image_path"] = tmp.name

    point = PointStruct(
        id=str(uuid.uuid4()),
        vector=vectors,
        payload=payload
    )
    qclient.upsert(collection_name=COLLECTION_NAME, points=[point])

    return "✅ Item added successfully!"

def search_items(query_text, query_image, max_results, min_score):
    """Search lost items by text or image"""
    vectors = None

    if query_text:
        vectors = embed_text(query_text)

    elif query_image:
        vectors = embed_image(query_image)

    else:
        return ["⚠️ Provide text or image to search."]

    results = qclient.search(
        collection_name=COLLECTION_NAME,
        query_vector=vectors,
        limit=max_results,
        score_threshold=min_score,
    )

    if not results:
        return ["No matches found."]

    outputs = []
    for r in results:
        desc = r.payload.get("description", "No description")
        img = r.payload.get("image_path", None)
        score = round(r.score, 3)
        if img:
            outputs.append((img, f"{desc} (score: {score})"))
        else:
            outputs.append((None, f"{desc} (score: {score})"))

    return outputs

# --------------------------
# Gradio UI
# --------------------------
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    with gr.Tab("➕ Add Found Item"):
        with gr.Row():
            desc_in = gr.Textbox(label="Item Description")
            img_in = gr.Image(type="pil", label="Upload Image")
        add_btn = gr.Button("Add Item")
        add_out = gr.Textbox(label="Status")
        add_btn.click(fn=add_item, inputs=[desc_in, img_in], outputs=add_out)

    with gr.Tab("🔍 Search Lost Item"):
        query_text = gr.Textbox(label="Search by Text (optional)")
        query_img = gr.Image(type="pil", label="Search by Image (optional)")
        max_results = gr.Slider(1, 20, step=1, value=5, label="Max Results")
        min_score = gr.Slider(0.0, 1.0, step=0.01, value=0.3, label="Min Similarity Score")
        search_btn = gr.Button("Search")
        results_out = gr.Gallery(label="Search Results").style(grid=2, height="auto")
        search_btn.click(fn=search_items, inputs=[query_text, query_img, max_results, min_score], outputs=results_out)

    with gr.Tab("⚙️ Admin"):
        gr.Markdown("Admin dashboard (future expansion).")

# --------------------------
# Run App
# --------------------------
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)