# 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
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", "").strip()
QDRANT_URL = os.environ.get("QDRANT_URL", "").strip()
QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY", "").strip()

# -------------------------
# 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)

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

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

# Create collection if missing
try:
    if not qclient.collection_exists(COLLECTION):
        qclient.create_collection(
            collection_name=COLLECTION,
            vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
        )
except Exception as e:
    print("Error initializing Qdrant collection:", e)

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

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

def gen_tags_from_image_file(image_bytes: io.BytesIO) -> str:
    if genai_client is None:
        return ""
    try:
        file_obj = genai_client.files.upload(file=image_bytes)
        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, file_obj],
        )
        return response.text.strip()
    except Exception:
        return ""

# -------------------------
# 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:
        img_bytes = io.BytesIO()
        uploaded_image.convert("RGB").save(img_bytes, format="PNG")
        img_bytes.seek(0)

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

        payload["tags"] = gen_tags_from_image_file(img_bytes)
        payload["image_b64"] = base64.b64encode(img_bytes.getvalue()).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"] = ""

    try:
        point = PointStruct(id=item_id, vector=vec, payload=payload)
        qclient.upsert(collection_name=COLLECTION, points=[point], wait=True)
    except Exception as e:
        return f"Error saving to Qdrant: {e}"

    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 and len(query_text.strip()) > 0:
        qvec = embed_text(query_text).tolist()
    else:
        return "Please provide a query image or some query text."

    try:
        hits = qclient.search(collection_name=COLLECTION, query_vector=qvec, limit=limit)
    except Exception as e:
        return f"Error querying Qdrant: {e}"

    if not hits:
        return "No results."

    results = []
    for h in hits:
        payload = h.payload or {}
        score = getattr(h, "score", None)
        results.append(
            f"id:{h.id} score:{float(score) if score else None} mode:{payload.get('mode','')} tags:{payload.get('tags','')} text:{payload.get('text','')}"
        )
    return "\n\n".join(results)

# -------------------------
# Gradio UI
# -------------------------
with gr.Blocks(title="Lost & Found — Simple Helper") as demo:
    gr.Markdown("## Lost & Found Helper (image/text search) — upload items, then 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 (e.g. 'black backpack with blue zipper')", label="Description (optional)")
            add_btn = gr.Button("Add item")
            add_out = gr.Textbox(label="Add result", interactive=False)
        with gr.Column():
            gr.Markdown("### Search")
            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.Textbox(label="Search results", interactive=False)

    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)