Fix CLIrrr2 model issue in appetete333.py

app.py

@@ -1,89 +1,84 @@
-# app.py
 import os
 import uuid
 import io
-import base64 # <-- FIX: This was missing
+import base64
 from PIL import Image
 import gradio as gr
 from sentence_transformers import SentenceTransformer
-import google.generativeai as genai # <-- FIX: Correct import for the genai library
+import google.generativeai as genai
 from qdrant_client import QdrantClient
 from qdrant_client.http.models import VectorParams, Distance, PointStruct
 
-# Note: The QDRANT_URL, QDRANT_API_KEY, and GEMINI_API_KEY environment variables
-# must be set for this application to work correctly.
-
+# --- Configuration ---
 GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
 QDRANT_URL = os.environ.get("QDRANT_URL")
 QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY")
 
+# --- Model Loading and Client Initialization ---
 print("Loading CLIP model...")
 MODEL_ID = "sentence-transformers/clip-ViT-B-32-multilingual-v1"
 clip_model = SentenceTransformer(MODEL_ID)
 
-# Initialize the GenAI client with the correct API key
-genai.configure(api_key=GEMINI_API_KEY)
+# Configure the Gemini client
+if GEMINI_API_KEY:
+    genai.configure(api_key=GEMINI_API_KEY)
 
 if not QDRANT_URL:
     raise RuntimeError("Set QDRANT_URL env var")
 qclient = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
 
+# --- Qdrant Collection Setup ---
 COLLECTION = "lost_found_items"
 VECTOR_SIZE = 512
-# Only create the collection if it doesn't already exist
-if not qclient.get_collections().collections:
+if not qclient.collection_exists(COLLECTION):
+    print(f"Creating collection: {COLLECTION}")
     qclient.create_collection(
         collection_name=COLLECTION,
         vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
     )
 
+# --- Core Functions ---
 def embed_text(text: str):
+    """Generates an embedding for the given text."""
     return clip_model.encode(text, convert_to_numpy=True)
 
 def embed_image_pil(pil_img: Image.Image):
+    """Generates an embedding for the given PIL image."""
     return clip_model.encode(pil_img, convert_to_numpy=True)
 
-# FIX: This function is updated to take a PIL Image object directly and
-# uses an inlineData object for the Gemini API call, as file upload is
-# not supported for gemini-2.5-flash in this manner.
-def gen_tags_from_image(pil_img: Image.Image) -> str:
+def gen_tags_from_image_file(img_bytes: io.BytesIO) -> str:
+    """Generates descriptive tags for an image using the Gemini API."""
     if not GEMINI_API_KEY:
+        print("Warning: GEMINI_API_KEY not set. Skipping tag generation.")
         return ""
     try:
-        # Convert PIL Image to a byte array
-        img_bytes = io.BytesIO()
-        pil_img.save(img_bytes, format="PNG")
-        img_bytes.seek(0)
-        
-        # Use inlineData to pass the image to the model
-        model = genai.GenerativeModel("gemini-2.5-flash")
+        img = Image.open(img_bytes)
+        model = genai.GenerativeModel('gemini-pro-vision')
         prompt = ("Give 4 short tags (comma-separated) describing this item in the image. "
                   "Respond only with tags.")
-        image_part = {
-            "mime_type": "image/png",
-            "data": img_bytes.getvalue()
-        }
-        resp = model.generate_content([prompt, image_part])
+        resp = model.generate_content([prompt, img])
         return resp.text.strip()
     except Exception as e:
-        print(f"Error generating tags: {e}")
+        print(f"Error calling Gemini API: {e}")
         return ""
 
-def add_item(mode: str, uploaded_image, text_description: str):
+def add_item(mode: str, uploaded_image: Image.Image, text_description: str):
+    """Adds a new lost or found item to the database."""
+    if not uploaded_image and not text_description:
+        return "Error: Please provide either an image or a description."
+
     item_id = str(uuid.uuid4())
     payload = {"mode": mode, "text": text_description}
 
     if uploaded_image:
-        # Use the PIL image directly for embedding
+        img_bytes = io.BytesIO()
+        uploaded_image.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)
         
-        # FIX: Pass the PIL image object to the tag generation function
-        payload["tags"] = gen_tags_from_image(uploaded_image)
-        
-        # Convert the PIL image to base64 string for storage in payload
-        img_bytes = io.BytesIO()
-        uploaded_image.save(img_bytes, format="PNG")
         img_bytes.seek(0)
         payload["image_b64"] = base64.b64encode(img_bytes.read()).decode("utf-8")
     else:
@@ -96,43 +91,50 @@ def add_item(mode: str, uploaded_image, text_description: str):
 
     return f"Saved item id: {item_id}\nTags: {payload.get('tags','')}"
 
-def search_items(query_image, query_text, limit: int = 5):
+def search_items(query_image: Image.Image, query_text: str, limit: int = 5):
+    """Searches for similar items in the database."""
     if query_image:
         qvec = embed_image_pil(query_image).tolist()
     elif query_text:
         qvec = embed_text(query_text).tolist()
     else:
-        return "Provide query image or text."
+        return "Provide a query image or text to search."
 
     hits = qclient.search(collection_name=COLLECTION, query_vector=qvec, limit=limit)
     if not hits:
-        return "No results."
+        return "No results found."
 
     results = []
     for h in hits:
         payload = h.payload or {}
         score = getattr(h, "score", 0)
         results.append(
-            f"ID:{h.id}\nScore:{float(score):.4f}\nMode:{payload.get('mode','')}\n"
-            f"Tags:{payload.get('tags','')}\nText:{payload.get('text','')}\n"
+            f"ID: {h.id}\nScore: {float(score):.4f}\nMode: {payload.get('mode','')}\n"
+            f"Tags: {payload.get('tags','')}\nText: {payload.get('text','')}\n"
         )
 
     return "\n\n".join(results)
 
-with gr.Blocks() as demo:
+# --- Gradio UI ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("## Lost & Found Helper")
+    gr.Markdown("Add items that were lost or found, and search for them using a photo or description.")
+    
     with gr.Row():
-        with gr.Column():
-            mode = gr.Radio(["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")
-            add_btn = gr.Button("Add item")
-            add_out = gr.Textbox(interactive=False, label="Result")
-        with gr.Column():
-            query_img = gr.Image(type="pil", label="Search by image (optional)")
-            query_text = gr.Textbox(lines=2, label="Search by text (optional)")
-            search_btn = gr.Button("Search")
-            search_out = gr.Textbox(interactive=False, label="Search results")
+        with gr.Column(scale=1):
+            gr.Markdown("### Add an Item")
+            mode = gr.Radio(["lost", "found"], value="lost", label="I have...")
+            upload_img = gr.Image(type="pil", label="Item Photo (optional)")
+            text_desc = gr.Textbox(lines=2, placeholder="e.g., 'red backpack with a keychain'", label="Description")
+            add_btn = gr.Button("Add Item", variant="primary")
+            add_out = gr.Textbox(interactive=False, label="Result", lines=3)
+        
+        with gr.Column(scale=2):
+            gr.Markdown("### Search for an Item")
+            query_img = gr.Image(type="pil", label="Search by Image (optional)")
+            query_text = gr.Textbox(lines=2, placeholder="e.g., 'blue water bottle'", label="Search by Text (optional)")
+            search_btn = gr.Button("Search", variant="primary")
+            search_out = gr.Textbox(interactive=False, label="Search Results", lines=10)
 
     add_btn.click(add_item, inputs=[mode, upload_img, text_desc], outputs=[add_out])
     search_btn.click(search_items, inputs=[query_img, query_text], outputs=[search_out])