fixed eveerything

app.py

@@ -3,7 +3,6 @@ import uuid
 import gradio as gr
 import numpy as np
 from PIL import Image
-import qdrant_client
 from qdrant_client import QdrantClient
 from qdrant_client.http.models import VectorParams, Distance, PointStruct
 from sentence_transformers import SentenceTransformer
@@ -16,73 +15,64 @@ os.makedirs(UPLOAD_DIR, exist_ok=True)
 
 COLLECTION = "lost_and_found"
 
-# Qdrant client (in-memory for Spaces; replace with actual url/api_key if you use a remote Qdrant)
 qclient = QdrantClient(":memory:")
 
-# SentenceTransformer encoder (CLIP)
+# Load CLIP model
 encoder = SentenceTransformer("clip-ViT-B-32")
 
-# Create collection if missing (use the model vector size)
-VECTOR_SIZE = encoder.get_sentence_embedding_dimension()
+# 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:
+    VECTOR_SIZE = len(encoder.encode(["test"])[0])
+
+# Create collection if not exists
 if not qclient.collection_exists(COLLECTION):
     qclient.create_collection(
         collection_name=COLLECTION,
-        vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
+        vectors_config=VectorParams(size=int(VECTOR_SIZE), distance=Distance.COSINE),
     )
 
-
 # ===============================
 # Encoding function
-# (image handling MUST remain unchanged per request)
 # ===============================
 def encode_data(text=None, image=None):
-    """
-    Returns a vector (numpy array) for either a PIL Image, an image path (str),
-    or text (string). Image-handling kept exactly as requested.
-    """
-    # --- IMAGE branch (unchanged) ---
+    """Encode either text or image into embeddings"""
     if isinstance(image, Image.Image):
-        # NOTE: per your instruction, do not modify the image encoding logic
         return encoder.encode(image.convert("RGB"))
     if isinstance(image, str):
         return encoder.encode(Image.open(image).convert("RGB"))
-
-    # --- TEXT branch (safe to adjust) ---
     if text:
         return encoder.encode([text])[0]
-
     return None
 
-
 # ===============================
-# Add Item (finder uploads a found item)
+# Add Item
 # ===============================
 def add_item(text, image, uploader_name, uploader_phone):
     try:
         img_path = None
         vector = None
 
-        # If image provided (PIL), save and encode by image (image priority)
         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)
-
-        # If no image but text provided -> encode text
         elif text:
             vector = encode_data(text=text)
 
         if vector is None:
-            return "❌ Please provide at least an image or some text."
+            return "❌ Please provide at least an image or text."
 
-        # Ensure vector is numpy array
         vec = np.asarray(vector, dtype=float)
 
         payload = {
             "text": text or "",
-            "uploader_name": (uploader_name or "N/A"),
-            "uploader_phone": (uploader_phone or "N/A"),
+            "uploader_name": uploader_name or "N/A",
+            "uploader_phone": uploader_phone or "N/A",
             "image_path": img_path,
             "has_image": bool(img_path),
         }
@@ -96,18 +86,14 @@ def add_item(text, image, uploader_name, uploader_phone):
     except Exception as e:
         return f"❌ Error: {e}"
 
-
 # ===============================
-# Search (fixed to handle text OR image OR both)
-# - keep image coding intact (per request)
-# - if both text+image supplied, average normalized vectors (cross-modal)
+# Search Items
 # ===============================
 def search_items(text, image, max_results, min_score):
     try:
         text_vec = None
         img_vec = None
 
-        # get vectors (do not change image encoding)
         if isinstance(image, Image.Image):
             img_vec = encode_data(image=image)
             img_vec = np.asarray(img_vec, dtype=float)
@@ -115,23 +101,18 @@ def search_items(text, image, max_results, min_score):
             text_vec = encode_data(text=text)
             text_vec = np.asarray(text_vec, dtype=float)
 
-        # If both provided -> combine (normalize then average)
         if img_vec is not None and text_vec is not None:
-            # normalize
-            n1 = np.linalg.norm(img_vec) + 1e-12
-            n2 = np.linalg.norm(text_vec) + 1e-12
-            v1 = img_vec / n1
-            v2 = text_vec / n2
-            qvec = v1 + v2
-            qvec = qvec / (np.linalg.norm(qvec) + 1e-12)
+            # 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 "❌ Please provide an image or some text to search.", []
+            return "❌ Provide text or image to search.", []
 
-        # Run search
         hits = qclient.search(
             collection_name=COLLECTION,
             query_vector=qvec.tolist(),
@@ -144,12 +125,11 @@ def search_items(text, image, max_results, min_score):
             return "No matches found.", []
 
         result_texts = []
-        gallery_items = []  # list of image paths (or placeholders)
+        gallery_items = []
 
         for h in hits:
             payload = h.payload or {}
-            score = getattr(h, "score", None)
-            score_str = f"{float(score):.3f}" if score is not None else "N/A"
+            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"
 
@@ -162,18 +142,11 @@ def search_items(text, image, max_results, min_score):
             img_path = payload.get("image_path")
             if img_path and os.path.exists(img_path):
                 gallery_items.append(img_path)
-            else:
-                # append a small placeholder (you can also skip adding)
-                # Gradio can display an empty string but better to put a placeholder image path if desired
-                # We'll skip adding placeholders so gallery only shows real images
-                pass
 
         return "\n".join(result_texts), gallery_items
-
     except Exception as e:
         return f"❌ Error: {e}", []
 
-
 # ===============================
 # Clear DB
 # ===============================
@@ -183,30 +156,28 @@ def clear_database():
             qclient.delete_collection(COLLECTION)
         qclient.create_collection(
             collection_name=COLLECTION,
-            vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
+            vectors_config=VectorParams(size=int(VECTOR_SIZE), distance=Distance.COSINE),
         )
-        # delete uploaded images
         for f in os.listdir(UPLOAD_DIR):
             try:
                 os.remove(os.path.join(UPLOAD_DIR, f))
-            except Exception:
+            except:
                 pass
         return "🗑️ Database cleared!"
     except Exception as e:
         return f"❌ Error clearing DB: {e}"
 
-
 # ===============================
 # Gradio UI
 # ===============================
 with gr.Blocks() as demo:
-    gr.Markdown("## 🔎 Lost & Found — Add Found Items (finder) & Search (lost)")
+    gr.Markdown("## 🔎 Lost & Found")
 
     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="Your name (finder)")
-        uploader_phone = gr.Textbox(label="Your phone (finder)")
+        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])