4.0v

app.py

@@ -7,31 +7,21 @@ 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)
+# CONFIG
 # -------------------------
 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)
-
-# Dynamically detect vector size
 VECTOR_SIZE = clip_model.get_sentence_embedding_dimension()
 
 genai_client = genai.Client(api_key=GEMINI_API_KEY) if GEMINI_API_KEY else None
@@ -42,7 +32,6 @@ if not QDRANT_URL:
 qclient = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
 COLLECTION = "lost_found_items"
 
-# Ensure collection exists with correct vector size
 try:
     if not qclient.collection_exists(COLLECTION):
         qclient.create_collection(
@@ -70,8 +59,7 @@ 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 (e.g. 'black backpack', 'water bottle'). "
-            "Respond only with tags, no extra explanation."
+            "Tags should be short single words or two-word phrases. Respond only with tags."
         )
         response = genai_client.models.generate_content(
             model="gemini-2.5-flash",
@@ -90,12 +78,17 @@ def decode_image_from_b64(b64_str: str):
         return None
 
 # -------------------------
-# App logic: add item
+# Add item
 # -------------------------
-def add_item(mode: str, uploaded_image, text_description: str):
+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
+    if mode == "found":
+        payload["finder_name"] = finder_name
+        payload["finder_phone"] = finder_phone
+
     try:
         if uploaded_image is not None:
             img_bytes = io.BytesIO()
@@ -121,7 +114,6 @@ def add_item(mode: str, uploaded_image, text_description: str):
             else:
                 payload["tags"] = ""
 
-        # Upsert into Qdrant
         point = PointStruct(id=item_id, vector=vec, payload=payload)
         qclient.upsert(collection_name=COLLECTION, points=[point], wait=True)
 
@@ -130,7 +122,7 @@ def add_item(mode: str, uploaded_image, text_description: str):
         return f"❌ Error saving to Qdrant: {e}"
 
 # -------------------------
-# App logic: search
+# Search
 # -------------------------
 def search_items(query_image, query_text, limit: int = 5, min_score: float = 0.90):
     if query_image is not None:
@@ -156,6 +148,11 @@ def search_items(query_image, query_text, limit: int = 5, min_score: float = 0.9
 
         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')}"
+
         captions.append(caption)
 
         if payload.get("has_image") and payload.get("image_b64"):
@@ -165,7 +162,6 @@ def search_items(query_image, query_text, limit: int = 5, min_score: float = 0.9
             else:
                 images.append(Image.new("RGB", (200,200), color="gray"))
         else:
-            # Placeholder for text-only entries
             img = Image.new("RGB", (200,200), color="lightblue")
             images.append(img)
 
@@ -183,7 +179,9 @@ with gr.Blocks(title="Lost & Found — Simple Helper") as demo:
         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)")
+            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")
             add_out = gr.Textbox(label="Add result", interactive=False)
         with gr.Column():
@@ -202,8 +200,16 @@ with gr.Blocks(title="Lost & Found — Simple Helper") as demo:
             )
             search_msg = gr.Textbox(label="Message", interactive=False)
 
-    add_btn.click(add_item, inputs=[mode, upload_img, text_desc], outputs=[add_out])
-    search_btn.click(search_items, inputs=[query_img, query_text, limit_slider, score_slider], outputs=[gallery, search_msg])
+    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]
+    )
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)