app.py

import os
import uuid
import gradio as gr
from PIL import Image
from qdrant_client import QdrantClient, models
from transformers import CLIPProcessor, CLIPModel

# ==============================
# Setup
# ==============================
UPLOAD_DIR = "uploaded_images"
os.makedirs(UPLOAD_DIR, exist_ok=True)

qclient = QdrantClient(":memory:")
COLLECTION = "lost_and_found"

# Create collection (with deprecation fix)
if not qclient.collection_exists(COLLECTION):
    qclient.create_collection(
        COLLECTION,
        vectors_config=models.VectorParams(size=512, distance=models.Distance.COSINE),
    )

# Load CLIP
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_proc = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")


# ==============================
# Encode Function
# ==============================
def encode_data(text=None, image=None):
    if text:
        inputs = clip_proc(text=[text], return_tensors="pt", padding=True)
        return clip_model.get_text_features(**inputs).detach().numpy()[0]

    if image:
        inputs = clip_proc(images=image, return_tensors="pt")
        return clip_model.get_image_features(**inputs).detach().numpy()[0]

    raise ValueError("Need either text or image for encoding")


# ==============================
# Add Item
# ==============================
def add_item(mode, text, image, uploader_name, uploader_phone):
    try:
        img_path = None
        if image:
            img_id = str(uuid.uuid4())
            img_path = os.path.join(UPLOAD_DIR, f"{img_id}.png")
            image.save(img_path)

        vector = encode_data(text=text if text else None, image=image if image else None)

        qclient.upsert(
            collection_name=COLLECTION,
            points=[
                models.PointStruct(
                    id=str(uuid.uuid4()),
                    vector=vector.tolist(),
                    payload={
                        "mode": mode,
                        "text": text or "",
                        "uploader_name": uploader_name or "N/A",
                        "uploader_phone": uploader_phone or "N/A",
                        "image_path": img_path,
                        "has_image": bool(image),
                    },
                )
            ],
        )
        return f"✅ Added successfully as {mode}!"
    except Exception as e:
        return f"❌ Error: {e}"


# ==============================
# Search Items
# ==============================
def search_items(text, image, max_results, min_score):
    try:
        vector = encode_data(text=text if text else None, image=image if image else None)

        results = qclient.search(
            collection_name=COLLECTION,
            query_vector=vector.tolist(),
            limit=max_results,
            score_threshold=min_score,
            with_payload=True,
        )

        texts, imgs = [], []
        for r in results:
            p = r.payload
            desc = (
                f"id:{r.id} | score:{r.score:.3f} | mode:{p.get('mode','')} | text:{p.get('text','')}"
            )

            # Always show uploader details
            uploader_name = p.get("uploader_name", "N/A") or "N/A"
            uploader_phone = p.get("uploader_phone", "N/A") or "N/A"
            desc += f" | uploader:{uploader_name} ({uploader_phone})"

            texts.append(desc)
            if p.get("has_image") and "image_path" in p:
                imgs.append(p["image_path"])
        return "\n".join(texts) if texts else "No matches", imgs
    except Exception as e:
        return f"❌ Error: {e}", []


# ==============================
# Delete All
# ==============================
def clear_all():
    qclient.recreate_collection(
        COLLECTION, vectors_config=models.VectorParams(size=512, distance=models.Distance.COSINE)
    )
    return "🗑️ All items cleared."


# ==============================
# Gradio UI
# ==============================
with gr.Blocks() as demo:
    gr.Markdown("# 🔑 Lost & Found Image/Text Search")

    with gr.Tab("➕ Add Item"):
        mode = gr.Radio(["lost", "found"], label="Mode", value="found")
        text_in = gr.Textbox(label="Description (optional)")
        img_in = gr.Image(type="pil", label="Upload Image")
        uploader_name = gr.Textbox(label="Your Name")
        uploader_phone = gr.Textbox(label="Your Phone")
        add_btn = gr.Button("Add to Database")
        add_out = gr.Textbox(label="Status")

        add_btn.click(
            add_item,
            inputs=[mode, text_in, img_in, uploader_name, uploader_phone],
            outputs=add_out,
        )

    with gr.Tab("🔍 Search"):
        search_text = gr.Textbox(label="Search by text (optional)")
        search_img = gr.Image(type="pil", label="Search by image (optional)")
        max_results = gr.Slider(1, 10, value=5, step=1, label="Max results")
        min_score = gr.Slider(0.5, 1.0, value=0.8, step=0.01, label="Min similarity threshold")
        search_btn = gr.Button("Search")
        search_out = gr.Textbox(label="Search results (text)")
        search_gallery = gr.Gallery(label="Search Results")

        search_btn.click(
            search_items,
            inputs=[search_text, search_img, max_results, min_score],
            outputs=[search_out, search_gallery],
        )

    with gr.Tab("🗑️ Admin"):
        clear_btn = gr.Button("Clear All Items")
        clear_out = gr.Textbox(label="Status")
        clear_btn.click(clear_all, outputs=clear_out)


# ==============================
# Launch
# ==============================
if __name__ == "__main__":
    demo.launch()