v5

app.py

@@ -7,22 +7,29 @@ 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
+# 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)
-VECTOR_SIZE = clip_model.get_sentence_embedding_dimension()
 
 genai_client = genai.Client(api_key=GEMINI_API_KEY) if GEMINI_API_KEY else None
 
@@ -31,7 +38,9 @@ if not QDRANT_URL:
 
 qclient = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
 COLLECTION = "lost_found_items"
+VECTOR_SIZE = clip_model.get_sentence_embedding_dimension()
 
+# Create collection if missing
 try:
     if not qclient.collection_exists(COLLECTION):
         qclient.create_collection(
@@ -45,12 +54,11 @@ except Exception as e:
 # Helpers
 # -------------------------
 def embed_text(text: str):
-    return clip_model.encode([text], convert_to_numpy=True)[0]
+    return clip_model.encode(text, convert_to_numpy=True)
 
 def embed_image_pil(pil_img: Image.Image):
     pil_img = pil_img.convert("RGB")
-    np_img = np.array(pil_img)
-    return clip_model.encode([np_img], convert_to_numpy=True)[0]
+    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:
@@ -59,70 +67,64 @@ def gen_tags_from_image_file(image_bytes: io.BytesIO) -> str:
         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. Respond only with tags."
+            "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 as e:
-        print("Error generating tags:", e)
-        return ""
-
-def decode_image_from_b64(b64_str: str):
-    try:
-        img_bytes = base64.b64decode(b64_str)
-        return Image.open(io.BytesIO(img_bytes))
     except Exception:
-        return None
+        return ""
 
 # -------------------------
-# Add item
+# 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}
 
-    # If "found", save finder info
+    # Found item extra details
     if mode == "found":
         payload["finder_name"] = finder_name
         payload["finder_phone"] = finder_phone
 
-    try:
-        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:
+    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)
-
-        return f"✅ Saved item id: {item_id}\nTags: {payload.get('tags','')}"
     except Exception as e:
-        return f"❌ Error saving to Qdrant: {e}"
+        return f"Error saving to Qdrant: {e}"
+
+    return f"✅ Saved item id: {item_id}\nTags: {payload.get('tags','')}"
 
 # -------------------------
-# Search
+# App logic: search
 # -------------------------
 def search_items(query_image, query_text, limit: int = 5, min_score: float = 0.90):
     if query_image is not None:
@@ -130,86 +132,85 @@ def search_items(query_image, query_text, limit: int = 5, min_score: float = 0.9
     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."
+        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}"
+        return f"❌ Error querying Qdrant: {e}", []
 
     if not hits:
-        return [], "No results found."
+        return "No results found.", []
 
-    images, captions = [], []
+    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 {}
-        caption = f"ID: {h.id}\nScore: {score:.4f}\nMode: {payload.get('mode','')}\nTags: {payload.get('tags','')}\nText: {payload.get('text','')}"
-
-        # If it's a found item, show finder details
-        if payload.get("mode") == "found":
-            caption += f"\n👤 Finder: {payload.get('finder_name','N/A')} | 📞 {payload.get('finder_phone','N/A')}"
+        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)
 
-        captions.append(caption)
+        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 payload.get("has_image") and payload.get("image_b64"):
-            img = decode_image_from_b64(payload["image_b64"])
-            if img:
-                images.append(img)
-            else:
-                images.append(Image.new("RGB", (200,200), color="gray"))
-        else:
-            img = Image.new("RGB", (200,200), color="lightblue")
-            images.append(img)
+    if not results_text:
+        return f"No results above similarity threshold {min_score}", []
 
-    if not images:
-        return [], f"No results above similarity threshold {min_score:.2f}"
+    return "\n\n".join(results_text), results_imgs
 
-    return list(zip(images, captions)), ""
+# -------------------------
+# App logic: clear images
+# -------------------------
+def clear_all_images():
+    try:
+        qclient.delete(
+            collection_name=COLLECTION,
+            points_selector={
+                "filter": {"must": [{"key": "has_image", "match": {"value": True}}]}
+            }
+        )
+        return "🗑️ All items with images have been cleared!"
+    except Exception as e:
+        return f"❌ Error while clearing images: {e}"
 
 # -------------------------
 # Gradio UI
 # -------------------------
 with gr.Blocks(title="Lost & Found — Simple Helper") as demo:
-    gr.Markdown("## 🧳 Lost & Found Helper — Upload items, then search by image or text.")
+    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", label="Description (optional)")
-            finder_name = gr.Textbox(lines=1, placeholder="Finder name (only if found)", label="Finder Name")
-            finder_phone = gr.Textbox(lines=1, placeholder="Finder phone (only if found)", label="Finder Phone")
-            add_btn = gr.Button("➕ Add item")
+            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")
+            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)")
-            limit_slider = gr.Slider(1, 10, value=5, step=1, label="Max results")
-            score_slider = gr.Slider(0.0, 1.0, value=0.90, step=0.01, label="Min similarity score")
-            search_btn = gr.Button("🔎 Search")
-            gallery = gr.Gallery(
-                label="Search Results",
-                show_label=True,
-                elem_id="gallery",
-                columns=2,
-                height="auto"
-            )
-            search_msg = gr.Textbox(label="Message", interactive=False)
-
-    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, limit_slider, score_slider],
-        outputs=[gallery, search_msg]
-    )
+            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)