fixed2

app.py

@@ -1,121 +1,160 @@
 import os
-import uuid
 import gradio as gr
-import numpy as np
-from PIL import Image
 from qdrant_client import QdrantClient
-from qdrant_client.http.models import VectorParams, Distance, PointStruct
+from qdrant_client.http import models as rest
+from qdrant_client.http.models import Distance, VectorParams
 from sentence_transformers import SentenceTransformer
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+import uuid
 
-# ===============================
-# Config
-# ===============================
-UPLOAD_DIR = "uploaded_images"
-os.makedirs(UPLOAD_DIR, exist_ok=True)
-
+# ----------------------------
+# Qdrant Setup
+# ----------------------------
+QDRANT_HOST = "localhost"
+QDRANT_PORT = 6333
 COLLECTION = "lost_and_found"
-qclient = QdrantClient(":memory:")
 
-# Load CLIP model
-encoder = SentenceTransformer("clip-ViT-B-32")
+qclient = QdrantClient(QDRANT_HOST, port=QDRANT_PORT)
+
+# Load Models
+text_model = SentenceTransformer("all-MiniLM-L6-v2")
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
 
-# Safe vector size
-VECTOR_SIZE = encoder.get_sentence_embedding_dimension()
-if VECTOR_SIZE is None:
-    VECTOR_SIZE = len(encoder.encode(["test"])[0])
+# Embedding sizes
+TEXT_VECTOR_SIZE = text_model.get_sentence_embedding_dimension()
+IMAGE_VECTOR_SIZE = clip_model.config.projection_dim
 
-# Create collection
-if not qclient.collection_exists(COLLECTION):
+# Create collection if not exists
+try:
+    qclient.get_collection(COLLECTION)
+except Exception:
     qclient.create_collection(
-        collection_name=COLLECTION,
-        vectors_config=VectorParams(size=int(VECTOR_SIZE), distance=Distance.COSINE),
+        COLLECTION,
+        vectors_config={
+            "text": VectorParams(size=TEXT_VECTOR_SIZE, distance=Distance.COSINE),
+            "image": VectorParams(size=IMAGE_VECTOR_SIZE, distance=Distance.COSINE),
+        },
     )
 
-# ===============================
-# Encode function
-# ===============================
+# ----------------------------
+# Encoding Helpers
+# ----------------------------
 def encode_text(text: str):
-    return np.asarray(encoder.encode([text])[0], dtype=float)
+    return text_model.encode([text])[0]
 
-def encode_image(img: Image.Image):
-    return np.asarray(encoder.encode(img.convert("RGB")), dtype=float)
+def encode_image(image: Image.Image):
+    inputs = clip_processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        emb = clip_model.get_image_features(**inputs)
+    return emb[0].cpu().numpy()
 
-# ===============================
-# Add Item
-# ===============================
+# ----------------------------
+# Add Found Item
+# ----------------------------
 def add_item(text, image, uploader_name, uploader_phone):
     try:
-        vector = None
-        img_path = None
+        if not text and image is None:
+            return "❌ Please provide a description or an image."
 
-        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_image(image)
-        elif text and text.strip():
-            vector = encode_text(text)
+        text_vector = encode_text(text) if text else None
+        image_vector = encode_image(image) if image is not None else None
 
-        if vector is None:
-            return "❌ Please provide either an image or description."
+        # Save uploaded image
+        img_path = None
+        if image is not None:
+            os.makedirs("uploaded_images", exist_ok=True)
+            img_id = str(uuid.uuid4()) + ".png"
+            img_path = os.path.join("uploaded_images", img_id)
+            image.save(img_path)
 
-        payload = {
-            "text": text or "",
-            "uploader_name": uploader_name or "N/A",
-            "uploader_phone": uploader_phone or "N/A",
-            "image_path": img_path,
-        }
+        vectors = {}
+        if text_vector is not None:
+            vectors["text"] = text_vector.tolist()
+        if image_vector is not None:
+            vectors["image"] = image_vector.tolist()
 
         qclient.upsert(
             collection_name=COLLECTION,
-            points=[PointStruct(id=str(uuid.uuid4()), vector=vector.tolist(), payload=payload)],
-            wait=True,
+            points=[
+                rest.PointStruct(
+                    id=str(uuid.uuid4()),
+                    vector=vectors,
+                    payload={
+                        "text": text,
+                        "image_path": img_path,
+                        "uploader_name": uploader_name or "N/A",
+                        "uploader_phone": uploader_phone or "N/A",
+                    },
+                )
+            ],
         )
-        return "✅ Item added!"
+        return "✅ Item added successfully!"
     except Exception as e:
-        return f"❌ Error: {e}"
+        return f"❌ Error adding item: {e}"
 
-# ===============================
-# Search Items
-# ===============================
+# ----------------------------
+# Search Lost Items
+# ----------------------------
 def search_items(text, image, max_results, min_score):
     try:
         vector = None
+        query_text = text.strip() if text else ""
+
         if isinstance(image, Image.Image):
             vector = encode_image(image)
-        elif text and text.strip():
+        elif text:
             vector = encode_text(text)
 
-        if vector is None:
-            return "❌ Provide text or image.", []
+        results = []
 
-        results = qclient.search(
-            collection_name=COLLECTION,
-            query_vector=vector.tolist(),
-            limit=int(max_results),
-            score_threshold=float(min_score),
-            with_payload=True,
-        )
+        # 1. Vector search
+        if vector is not None:
+            results = qclient.search(
+                collection_name=COLLECTION,
+                query_vector=vector.tolist(),
+                limit=int(max_results),
+                score_threshold=float(min_score),
+                with_payload=True,
+            )
+
+        # 2. Fallback text search on payload
+        if query_text:
+            keyword_results = qclient.scroll(
+                collection_name=COLLECTION,
+                scroll_filter=rest.Filter(
+                    must=[rest.FieldCondition(
+                        key="text",
+                        match=rest.MatchText(text=query_text)
+                    )]
+                ),
+                limit=100,
+                with_payload=True
+            )[0]
+
+            existing_ids = {r.id for r in results}
+            for km in keyword_results:
+                if km.id not in existing_ids:
+                    km.score = 1.0
+                    results.append(km)
 
         if not results:
             return "No matches found.", []
 
-        text_out = []
-        gallery = []
-
-        for r in results:
+        # Format output
+        text_out, gallery = [], []
+        for r in results[:max_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:{r.id} | score:{score:.3f} | "
                 f"text:{payload.get('text','')} | "
                 f"finder:{uploader_name} ({uploader_phone})"
             )
             text_out.append(desc)
-
             img_path = payload.get("image_path")
             if img_path and os.path.exists(img_path):
                 gallery.append(img_path)
@@ -124,55 +163,40 @@ def search_items(text, image, max_results, min_score):
     except Exception as e:
         return f"❌ Error: {e}", []
 
-# ===============================
-# Clear DB
-# ===============================
-def clear_database():
-    try:
-        if qclient.collection_exists(COLLECTION):
-            qclient.delete_collection(COLLECTION)
-        qclient.create_collection(
-            collection_name=COLLECTION,
-            vectors_config=VectorParams(size=int(VECTOR_SIZE), distance=Distance.COSINE),
-        )
-        for f in os.listdir(UPLOAD_DIR):
-            try:
-                os.remove(os.path.join(UPLOAD_DIR, f))
-            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 System")
-
+# ----------------------------
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
     with gr.Tab("➕ Add Found Item"):
-        text_in = gr.Textbox(label="Description (optional)")
-        img_in = gr.Image(type="pil", label="Upload Image (optional)")
+        desc_in = gr.Textbox(label="Description", placeholder="Describe the item...")
+        img_in = gr.Image(label="Upload Image", type="pil")
         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])
+        add_btn = gr.Button("Add Item")
+        add_out = gr.Textbox(label="Status")
+        add_btn.click(
+            add_item,
+            inputs=[desc_in, img_in, uploader_name, uploader_phone],
+            outputs=[add_out]
+        )
 
     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=0.3, step=0.01, label="Min Similarity Score")
+        text_in = gr.Textbox(label="Search by Text (optional)")
+        img_in_search = gr.Image(label="Search by Image (optional)", type="pil")
+        max_res = gr.Slider(1, 20, value=5, step=1, label="Max Results")
+        min_score = gr.Slider(0, 1, 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_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])
+        result_text = gr.Textbox(label="Search Results (Text)")
+        result_gallery = gr.Gallery(label="Search Results (Images)").style(grid=3)
+        search_btn.click(
+            search_items,
+            inputs=[text_in, img_in_search, max_res, min_score],
+            outputs=[result_text, result_gallery]
+        )
 
-    with gr.Tab("🗑️ Admin"):
-        clear_btn = gr.Button("Clear Database")
-        clear_out = gr.Textbox(label="Status")
-        clear_btn.click(clear_database, outputs=[clear_out])
+    with gr.Tab("⚙️ Admin"):
+        gr.Markdown("Admin dashboard placeholder...")
 
 if __name__ == "__main__":
+    import torch
     demo.launch(server_name="0.0.0.0", server_port=7860)