fixed

app.py

@@ -8,27 +8,23 @@ from qdrant_client.http.models import VectorParams, Distance, PointStruct
 from sentence_transformers import SentenceTransformer
 
 # ===============================
-# Config / Setup
+# Config
 # ===============================
 UPLOAD_DIR = "uploaded_images"
 os.makedirs(UPLOAD_DIR, exist_ok=True)
 
 COLLECTION = "lost_and_found"
-
 qclient = QdrantClient(":memory:")
 
 # Load CLIP model
 encoder = SentenceTransformer("clip-ViT-B-32")
 
-# Get vector dimension safely
-try:
-    VECTOR_SIZE = encoder.get_sentence_embedding_dimension()
-    if VECTOR_SIZE is None:
-        VECTOR_SIZE = len(encoder.encode(["test"])[0])
-except Exception:
+# Safe vector size
+VECTOR_SIZE = encoder.get_sentence_embedding_dimension()
+if VECTOR_SIZE is None:
     VECTOR_SIZE = len(encoder.encode(["test"])[0])
 
-# Create collection if not exists
+# Create collection
 if not qclient.collection_exists(COLLECTION):
     qclient.create_collection(
         collection_name=COLLECTION,
@@ -36,53 +32,46 @@ if not qclient.collection_exists(COLLECTION):
     )
 
 # ===============================
-# Encoding function
+# Encode function
 # ===============================
-def encode_data(text=None, image=None):
-    """Encode either text or image into embeddings"""
-    if isinstance(image, Image.Image):
-        return encoder.encode(image.convert("RGB"))
-    if isinstance(image, str):
-        return encoder.encode(Image.open(image).convert("RGB"))
-    if text:
-        return encoder.encode([text])[0]
-    return None
+def encode_text(text: str):
+    return np.asarray(encoder.encode([text])[0], dtype=float)
+
+def encode_image(img: Image.Image):
+    return np.asarray(encoder.encode(img.convert("RGB")), dtype=float)
 
 # ===============================
 # Add Item
 # ===============================
 def add_item(text, image, uploader_name, uploader_phone):
     try:
-        img_path = None
         vector = None
+        img_path = None
 
         if isinstance(image, Image.Image):
             img_id = str(uuid.uuid4())
             img_path = os.path.join(UPLOAD_DIR, f"{img_id}.png")
             image.save(img_path)
-            vector = encode_data(image=image)
-        elif text:
-            vector = encode_data(text=text)
+            vector = encode_image(image)
+        elif text and text.strip():
+            vector = encode_text(text)
 
         if vector is None:
-            return "❌ Please provide at least an image or text."
-
-        vec = np.asarray(vector, dtype=float)
+            return "❌ Please provide either an image or description."
 
         payload = {
             "text": text or "",
             "uploader_name": uploader_name or "N/A",
             "uploader_phone": uploader_phone or "N/A",
             "image_path": img_path,
-            "has_image": bool(img_path),
         }
 
         qclient.upsert(
             collection_name=COLLECTION,
-            points=[PointStruct(id=str(uuid.uuid4()), vector=vec.tolist(), payload=payload)],
+            points=[PointStruct(id=str(uuid.uuid4()), vector=vector.tolist(), payload=payload)],
             wait=True,
         )
-        return "✅ Item added to database!"
+        return "✅ Item added!"
     except Exception as e:
         return f"❌ Error: {e}"
 
@@ -91,59 +80,47 @@ def add_item(text, image, uploader_name, uploader_phone):
 # ===============================
 def search_items(text, image, max_results, min_score):
     try:
-        text_vec = None
-        img_vec = None
-
+        vector = None
         if isinstance(image, Image.Image):
-            img_vec = encode_data(image=image)
-            img_vec = np.asarray(img_vec, dtype=float)
-        if text and len(text.strip()) > 0:
-            text_vec = encode_data(text=text)
-            text_vec = np.asarray(text_vec, dtype=float)
-
-        if img_vec is not None and text_vec is not None:
-            # Combine both queries
-            v1 = img_vec / (np.linalg.norm(img_vec) + 1e-12)
-            v2 = text_vec / (np.linalg.norm(text_vec) + 1e-12)
-            qvec = (v1 + v2) / 2
-        elif img_vec is not None:
-            qvec = img_vec
-        elif text_vec is not None:
-            qvec = text_vec
-        else:
-            return "❌ Provide text or image to search.", []
-
-        hits = qclient.search(
+            vector = encode_image(image)
+        elif text and text.strip():
+            vector = encode_text(text)
+
+        if vector is None:
+            return "❌ Provide text or image.", []
+
+        results = qclient.search(
             collection_name=COLLECTION,
-            query_vector=qvec.tolist(),
+            query_vector=vector.tolist(),
             limit=int(max_results),
             score_threshold=float(min_score),
             with_payload=True,
         )
 
-        if not hits:
+        if not results:
             return "No matches found.", []
 
-        result_texts = []
-        gallery_items = []
+        text_out = []
+        gallery = []
 
-        for h in hits:
-            payload = h.payload or {}
-            score_str = f"{getattr(h, 'score', 0):.3f}"
-            uploader_name = payload.get("uploader_name", "N/A") or "N/A"
-            uploader_phone = payload.get("uploader_phone", "N/A") or "N/A"
+        for r in results:
+            payload = r.payload or {}
+            score = getattr(r, "score", 0)
+            uploader_name = payload.get("uploader_name", "N/A")
+            uploader_phone = payload.get("uploader_phone", "N/A")
 
             desc = (
-                f"id:{h.id} | score:{score_str} | text:{payload.get('text','')} "
-                f"| finder:{uploader_name} ({uploader_phone})"
+                f"id:{r.id} | score:{score:.3f} | "
+                f"text:{payload.get('text','')} | "
+                f"finder:{uploader_name} ({uploader_phone})"
             )
-            result_texts.append(desc)
+            text_out.append(desc)
 
             img_path = payload.get("image_path")
             if img_path and os.path.exists(img_path):
-                gallery_items.append(img_path)
+                gallery.append(img_path)
 
-        return "\n".join(result_texts), gallery_items
+        return "\n".join(text_out), gallery
     except Exception as e:
         return f"❌ Error: {e}", []
 
@@ -171,29 +148,29 @@ def clear_database():
 # Gradio UI
 # ===============================
 with gr.Blocks() as demo:
-    gr.Markdown("## 🔎 Lost & Found")
+    gr.Markdown("## 🔎 Lost & Found System")
 
     with gr.Tab("➕ Add Found Item"):
         text_in = gr.Textbox(label="Description (optional)")
         img_in = gr.Image(type="pil", label="Upload Image (optional)")
-        uploader_name = gr.Textbox(label="Finder's name")
-        uploader_phone = gr.Textbox(label="Finder's phone")
-        add_btn = gr.Button("Add to database")
+        uploader_name = gr.Textbox(label="Finder's Name")
+        uploader_phone = gr.Textbox(label="Finder's Phone")
+        add_btn = gr.Button("Add to Database")
         add_status = gr.Textbox(label="Status")
         add_btn.click(add_item, inputs=[text_in, img_in, uploader_name, uploader_phone], outputs=[add_status])
 
     with gr.Tab("🔍 Search Lost Item"):
-        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, 20, value=5, step=1, label="Max results")
-        min_score = gr.Slider(0.0, 1.0, value=0.75, step=0.01, label="Min similarity score")
+        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, 20, value=5, step=1, label="Max Results")
+        min_score = gr.Slider(0.0, 1.0, value=0.3, step=0.01, label="Min Similarity Score")
         search_btn = gr.Button("Search")
-        search_text_out = gr.Textbox(label="Search results (text)")
+        search_text_out = gr.Textbox(label="Search Results (Text)")
         search_gallery = gr.Gallery(label="Search Results", columns=2, height="auto")
         search_btn.click(search_items, inputs=[search_text, search_img, max_results, min_score], outputs=[search_text_out, search_gallery])
 
     with gr.Tab("🗑️ Admin"):
-        clear_btn = gr.Button("Clear database")
+        clear_btn = gr.Button("Clear Database")
         clear_out = gr.Textbox(label="Status")
         clear_btn.click(clear_database, outputs=[clear_out])