v6

app.py

@@ -1,176 +1,85 @@
-# app.py
-import os
+import gradio as gr
 import uuid
-import io
-import base64
+from qdrant_client import QdrantClient
+from qdrant_client.models import PointStruct
+from sentence_transformers import SentenceTransformer
 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 = clip_model.get_sentence_embedding_dimension()
+# Connect to Qdrant
+COLLECTION = "lost_and_found"
+qclient = QdrantClient(":memory:")  # use in-memory for demo, replace with host/port for persistence
+
+# Load CLIP model
+model = SentenceTransformer("sentence-transformers/clip-ViT-B-32-multilingual-v1")
+
+# Ensure collection exists
+qclient.recreate_collection(
+    collection_name=COLLECTION,
+    vectors_config={"size": 512, "distance": "Cosine"}
+)
+
+# Encode helper
+def encode_data(text=None, image=None):
+    if image is not None:
+        img = Image.open(image).convert("RGB")
+        emb = model.encode(img, convert_to_numpy=True, normalize_embeddings=True)
+    elif text:
+        emb = model.encode(text, convert_to_numpy=True, normalize_embeddings=True)
+    else:
+        raise ValueError("Need text or image")
+    return emb.astype(np.float32)
 
-# Create collection if missing
-try:
-    if not qclient.collection_exists(COLLECTION):
-        qclient.create_collection(
+# Add item
+def add_item(mode, text, image, name, phone):
+    try:
+        vector = encode_data(text=text if text else None, image=image if image else None)
+        payload = {
+            "mode": mode,
+            "text": text,
+            "has_image": image is not None,
+        }
+        if mode == "found":
+            payload["finder_name"] = name
+            payload["finder_phone"] = phone
+        qclient.upsert(
             collection_name=COLLECTION,
-            vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
+            points=[PointStruct(id=str(uuid.uuid4()), vector=vector.tolist(), payload=payload)]
         )
-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)
+        return "✅ Item added successfully!"
+    except Exception as e:
+        return f"❌ Error: {e}"
 
-def gen_tags_from_image_file(image_bytes: io.BytesIO) -> str:
-    if genai_client is None:
-        return ""
+# Search items
+def search_items(query_image, query_text, limit, min_score):
     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."
+        query_vector = encode_data(
+            text=query_text if query_text else None,
+            image=query_image if query_image else None
         )
-        response = genai_client.models.generate_content(
-            model="gemini-2.5-flash",
-            contents=[prompt_text, file_obj],
+        results = qclient.search(
+            collection_name=COLLECTION,
+            query_vector=query_vector.tolist(),
+            limit=limit,
         )
-        return response.text.strip()
-    except Exception:
-        return ""
-
-# -------------------------
-# App logic: add item
-# -------------------------
-def add_item(mode: str, uploaded_image, text_description: str, finder_name: str, finder_phone: str):
-    item_id = str(uuid.uuid4())
-    payload = {"mode": mode, "text": text_description}
-
-    # Found item extra details
-    if mode == "found":
-        payload["finder_name"] = finder_name
-        payload["finder_phone"] = finder_phone
-
-    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)
+        out_texts, out_imgs = [], []
+        for r in results:
+            if r.score < min_score:
+                continue
+            pl = r.payload
+            info = f"id:{r.id} | score:{r.score:.4f} | mode:{pl.get('mode','')}"
+            if pl.get("text"):
+                info += f" | text:{pl['text']}"
+            if pl.get("mode") == "found":
+                info += f" | found by: {pl.get('finder_name','?')} ({pl.get('finder_phone','?')})"
+            out_texts.append(info)
+            if pl.get("has_image"):
+                out_imgs.append(query_image)  # just echo search image (or store actual image paths if needed)
+        return "\n".join(out_texts) if out_texts else "No matches.", out_imgs
     except Exception as e:
-        return f"Error saving to Qdrant: {e}"
+        return f"❌ Error: {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, min_score: float = 0.90):
-    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 found.", []
-
-    results_text = []
-    results_imgs = []
-    for h in hits:
-        score = getattr(h, "score", None)
-        if score is None or score < min_score:
-            continue  
-
-        payload = h.payload or {}
-        text_entry = (
-            f"id:{h.id} | score:{score:.4f} | mode:{payload.get('mode','')} | tags:{payload.get('tags','')} "
-            f"| text:{payload.get('text','')} | finder:{payload.get('finder_name','-')} | phone:{payload.get('finder_phone','-')}"
-        )
-        results_text.append(text_entry)
-
-        if payload.get("has_image") and "image_b64" in payload:
-            try:
-                img = Image.open(io.BytesIO(base64.b64decode(payload["image_b64"])))
-                results_imgs.append(img)
-            except Exception:
-                pass
-
-    if not results_text:
-        return f"No results above similarity threshold {min_score}", []
-
-    return "\n\n".join(results_text), results_imgs
-
-# -------------------------
-# App logic: clear images
-# -------------------------
+# Clear all images
 def clear_all_images():
     try:
         qclient.delete(
@@ -179,38 +88,37 @@ def clear_all_images():
                 "filter": {"must": [{"key": "has_image", "match": {"value": True}}]}
             }
         )
-        return "🗑️ All items with images have been cleared!"
+        return "🗑️ All image items cleared!"
     except Exception as e:
-        return f"❌ Error while clearing images: {e}"
+        return f"❌ Error clearing images: {e}"
 
-# -------------------------
 # 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)")
-            finder_name = gr.Textbox(label="Finder Name (only if found)", placeholder="e.g. John Doe")
-            finder_phone = gr.Textbox(label="Finder Phone (only if found)", placeholder="e.g. +1234567890")
-            add_btn = gr.Button("Add item")
-            add_out = gr.Textbox(label="Add result", interactive=False)
-            clear_btn = gr.Button("Clear All Images")
-            clear_out = gr.Textbox(label="Clear 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)")
-            score_slider = gr.Slider(0.5, 1.0, value=0.90, step=0.01, label="Min similarity threshold")
-            search_btn = gr.Button("Search")
-            search_out = gr.Textbox(label="Search results (text)", interactive=False)
-            gallery = gr.Gallery(label="Search Results", show_label=True, elem_id="gallery", columns=2, height="auto")
-
-    add_btn.click(add_item, inputs=[mode, upload_img, text_desc, finder_name, finder_phone], outputs=[add_out])
-    search_btn.click(search_items, inputs=[query_img, query_text, gr.Number(value=5, visible=False), score_slider], outputs=[search_out, gallery])
-    clear_btn.click(clear_all_images, outputs=[clear_out])
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+with gr.Blocks() as demo:
+    gr.Markdown("# 🔎 Lost & Found System")
+
+    with gr.Tab("➕ Add Item"):
+        mode = gr.Radio(["lost", "found"], label="Mode")
+        text = gr.Textbox(label="Describe the item (optional)")
+        img = gr.Image(type="filepath", label="Upload image (optional)")
+        name = gr.Textbox(label="Finder's Name (only if found)", placeholder="John Doe")
+        phone = gr.Textbox(label="Finder's Phone (only if found)", placeholder="+1234567890")
+        add_btn = gr.Button("Add Item")
+        add_out = gr.Textbox(label="Add result")
+        add_btn.click(add_item, inputs=[mode, text, img, name, phone], outputs=add_out)
+
+    with gr.Tab("🔍 Search"):
+        query_text = gr.Textbox(label="Search by text (optional)")
+        query_img = gr.Image(type="filepath", label="Search by image (optional)")
+        max_results = gr.Slider(1, 10, value=5, step=1, label="Max results")
+        score_slider = gr.Slider(0.5, 1.0, value=0.9, step=0.01, label="Min similarity threshold")
+        search_btn = gr.Button("Search")
+        search_out = gr.Textbox(label="Search results (text)")
+        gallery = gr.Gallery(label="Search Results", show_label=True, elem_id="gallery", columns=2, height="auto")
+        search_btn.click(search_items, inputs=[query_img, query_text, max_results, score_slider], outputs=[search_out, gallery])
+
+    with gr.Tab("🗑️ Admin"):
+        clear_btn = gr.Button("Clear All Images")
+        clear_out = gr.Textbox(label="Clear Result")
+        clear_btn.click(clear_all_images, outputs=clear_out)
+
+demo.launch()