app.py

# app.py
import os
import uuid
import io
import base64
from PIL import Image
import gradio as gr
import numpy as np

# CLIP via Sentence-Transformers (text+image to same 512-dim space)
from sentence_transformers import SentenceTransformer

# Gemini (Google) client
from google import genai

# Qdrant client & helpers
from qdrant_client import QdrantClient
from qdrant_client.http.models import VectorParams, Distance, PointStruct

# -------------------------
#  CONFIG (reads env vars)
# -------------------------
GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
QDRANT_URL = os.environ.get("QDRANT_URL")
QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY")

# -------------------------
#  Initialize clients/models
# -------------------------
print("Loading CLIP model (this may take 20-60s the first time)...")
MODEL_ID = "sentence-transformers/clip-ViT-B-32-multilingual-v1"
clip_model = SentenceTransformer(MODEL_ID)

# Gemini client
genai_client = genai.Client(api_key=GEMINI_API_KEY) if GEMINI_API_KEY else None

# Qdrant client
if not QDRANT_URL:
    raise RuntimeError("Please set QDRANT_URL environment variable")
qclient = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)

COLLECTION = "lost_found_items"
VECTOR_SIZE = 512

if not qclient.collection_exists(COLLECTION):
    qclient.create_collection(
        collection_name=COLLECTION,
        vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
    )

# -------------------------
#  Helpers
# -------------------------
def embed_text(text: str):
    return clip_model.encode(text, convert_to_numpy=True)

def embed_image_pil(pil_img: Image.Image):
    return clip_model.encode(pil_img, convert_to_numpy=True)

def gen_tags_from_image_file(file_obj) -> str:
    """file_obj can be path or BytesIO"""
    if genai_client is None:
        return ""
    uploaded_file = genai_client.files.upload(file=file_obj)
    prompt_text = (
        "Give 4 short tags (comma-separated) describing this item in the image. "
        "Tags should be short single words or two-word phrases (e.g. 'black backpack', 'water bottle'). "
        "Respond only with tags, no extra explanation."
    )
    response = genai_client.models.generate_content(
        model="gemini-2.5-flash",
        contents=[prompt_text, uploaded_file],
    )
    return response.text.strip()

# -------------------------
#  App logic: add item
# -------------------------
def add_item(mode: str, uploaded_image, text_description: str):
    item_id = str(uuid.uuid4())
    payload = {"mode": mode, "text": text_description}

    if uploaded_image is not None:
        # Save to BytesIO
        img_bytes_io = io.BytesIO()
        uploaded_image.save(img_bytes_io, format="PNG")
        img_bytes_io.seek(0)

        # Embed image
        vec = embed_image_pil(uploaded_image).tolist()
        payload["has_image"] = True

        # Generate tags
        try:
            tags = gen_tags_from_image_file(img_bytes_io)
        except Exception:
            tags = ""
        payload["tags"] = tags

        # Store image as base64
        img_bytes_io.seek(0)
        payload["image_b64"] = base64.b64encode(img_bytes_io.read()).decode("utf-8")
    else:
        vec = embed_text(text_description).tolist()
        payload["has_image"] = False

        if genai_client:
            try:
                resp = genai_client.models.generate_content(
                    model="gemini-2.5-flash",
                    contents=f"Give 4 short, comma-separated tags for this item described as: {text_description}. Reply only with tags."
                )
                payload["tags"] = resp.text.strip()
            except Exception:
                payload["tags"] = ""
        else:
            payload["tags"] = ""

    # Upsert into Qdrant
    point = PointStruct(id=item_id, vector=vec, payload=payload)
    qclient.upsert(collection_name=COLLECTION, points=[point], wait=True)

    return f"Saved item id: {item_id}\nTags: {payload.get('tags','')}"

# -------------------------
#  App logic: search
# -------------------------
def search_items(query_image, query_text, limit: int = 5):
    if query_image is not None:
        qvec = embed_image_pil(query_image).tolist()
    elif query_text:
        qvec = embed_text(query_text).tolist()
    else:
        return "Please provide a query image or text."

    hits = qclient.search(collection_name=COLLECTION, query_vector=qvec, limit=limit)

    if not hits:
        return "No results."

    results = []
    for h in hits:
        payload = h.payload or {}
        score = getattr(h, "score", None)
        img_html = ""
        if payload.get("has_image") and payload.get("image_b64"):
            img_html = f'<img src="data:image/png;base64,{payload["image_b64"]}" width="200">'
        results.append(
            f"{img_html}<br>ID:{h.id}<br>Score:{float(score) if score else 0:.4f}<br>"
            f"Mode:{payload.get('mode','')}<br>Tags:{payload.get('tags','')}<br>Text:{payload.get('text','')}"
        )

    return "<br><br>".join(results)

# -------------------------
#  Gradio UI
# -------------------------
with gr.Blocks(title="Lost & Found — Simple Helper") as demo:
    gr.Markdown("## Lost & Found Helper — Upload items and search by image or text.")
    with gr.Row():
        with gr.Column():
            mode = gr.Radio(choices=["lost", "found"], value="lost", label="Add as")
            upload_img = gr.Image(type="pil", label="Item photo (optional)")
            text_desc = gr.Textbox(lines=2, placeholder="Short description", label="Description (optional)")
            add_btn = gr.Button("Add item")
            add_out = gr.HTML(label="Add result")  # Changed to HTML to render images
        with gr.Column():
            query_img = gr.Image(type="pil", label="Search by image (optional)")
            query_text = gr.Textbox(lines=2, label="Search by text (optional)")
            search_btn = gr.Button("Search")
            search_out = gr.HTML(label="Search results")  # HTML to render images

    add_btn.click(add_item, inputs=[mode, upload_img, text_desc], outputs=[add_out])
    search_btn.click(search_items, inputs=[query_img, query_text], outputs=[search_out])

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