injection completed

.gitignore

@@ -4,3 +4,4 @@
 *pyc**
 **pycache**
 *agent/**
+**downloaded_images**

dev_backend/.env

@@ -1,9 +0,0 @@
-MYSQL_HOST=localhost
-
-MYSQL_PORT=3306
-
-MYSQL_USER=root
-
-MYSQL_PASSWORD='AmB@ig123'
-
-MYSQL_DATABASE=listing_radar

dev_backend/main.py

@@ -1,3 +1,9 @@
+import sys
+import os
+
+# Add the project root to sys.path to allow imports from model_export
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
 from fastapi import FastAPI, status
 from starlette.middleware.base import BaseHTTPMiddleware, RequestResponseEndpoint
 from dotenv import load_dotenv

dev_backend/vector_db_router/models.py

@@ -3,13 +3,24 @@ from qdrant_client.models import PointStruct
 from typing import Dict, Any
 
 class CollectionHandler:
-    def __init__(self, collection_name: str, vector: Any, vector_size: int, payload: Dict, id: int):
+    def __init__(self, collection_name: str, vector: Any, vector_size: int, payload: Dict,
+    id: int=None,
+    link: str=None,
+    asin: str=None,
+    category: str=None,
+    brand: str=None,
+    client: AsyncQdrantClient=None
+    ):
         self.collection_name = collection_name
         self.vector = vector
+        self.id = id
         self.vector_size = vector_size
         self.payload = payload
-        self.id = id
+        self.link = link
-        self.client = AsyncQdrantClient("localhost", port=6333)
+        self.asin = asin
+        self.category = category
+        self.brand = brand
+        self.client = client if client else AsyncQdrantClient("localhost", port=6333)
 
     async def create_collection(self):
         try:
@@ -18,22 +29,35 @@ class CollectionHandler:
             
             await self.client.create_collection(
                 collection_name=self.collection_name,
-                vectors_config=models.VectorParams(size=self.vector_size, distance=models.Distance.COSINE),
+                vectors_config=models.VectorParams(size=self.vector_size, distance=models.Distance.EUCLID),
                 optimizers_config=models.OptimizersConfigDiff(indexing_threshold=20000)
             )
             
             # Creating payload indexes as per project logic
+
             await self.client.create_payload_index(
                 collection_name=self.collection_name,
-                field_name="Product_ID",
+                field_name="link",
                 field_schema=models.PayloadSchemaType.KEYWORD
             )
             await self.client.create_payload_index(
                 collection_name=self.collection_name,
-                field_name="Product_Link",
+                field_name="title",
                 field_schema=models.PayloadSchemaType.KEYWORD
             )
 
+            await self.client.create_payload_index(
+                collection_name=self.collection_name,
+                field_name="brand",
+                field_schema=models.PayloadSchemaType.KEYWORD
+            )
+            await self.client.create_payload_index(
+                collection_name=self.collection_name,
+                field_name="asin",
+                field_schema=models.PayloadSchemaType.KEYWORD
+            )
+
+            
             return {"message": f"Collection {self.collection_name} created successfully"}
         except Exception as e:
             return {"message": str(e)}
@@ -46,10 +70,10 @@ class CollectionHandler:
                     PointStruct(id=self.id, vector=self.vector, payload=self.payload)
                 ]
             )
-            print("Data inserted successfully")
             return True
         except Exception as e:
-            print("Insertion failed: ", e)
+            # Note: In a real app we'd use a logger here
+            print(f"Insertion failed for ID {self.id}: {e}")
             return False
     
     async def upsert_point(self):

dev_backend/vector_db_router/views.py

@@ -9,9 +9,92 @@ from .serializers import (
     UpdateCollectionSerializer, 
     DeleteCollectionSerializer
 )
+from model_export.dino_image_matching import get_vectors
 from .models import CollectionHandler
-
+import os
 app_router = APIRouter()
+import logging
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
+log = logging.getLogger(__name__)
+
+import pandas as pd
+
+@app_router.get("/get_vectors")
+async def get_vectors_endpoint(
+    q: Annotated[AsyncQdrantClient, Depends(get_qdrant_client)],
+    image_path:str=os.getenv("DATASET")
+):
+    try:
+        # Construct path relative to this file
+        base_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        excel_path = os.path.join(base_root, "model_export", "listing_data.xlsx")
+        df = pd.read_excel(excel_path)
+        asin_list = df['ASIN'].dropna().astype(str).tolist()
+        log.info(f"Generating vectors and ingesting {len(asin_list)} ASINs into Qdrant from {excel_path}")
+        
+        # 1. Initialize/Create collection "Product"
+        # DINOv2 vitb14 vector size is 768
+        init_handler = CollectionHandler(
+            collection_name="Product",
+            vector=[],
+            vector_size=768,
+            payload={},
+            client=q
+        )
+        await init_handler.create_collection()
+        
+        result_lst = []
+        for index, row in df.iterrows():
+            asin = str(row['ASIN'])
+            if pd.isna(row['ASIN']):
+                continue
+                
+            title = str(row['Title'])
+            brand = str(row['Brand'])
+            link = str(row['Image'])
+            
+            # Call get_vectors
+            vector = get_vectors(image_input=image_path, item=asin)
+            
+            if vector is None:
+                log.warning(f"Skipping {asin} due to missing image/vector")
+                continue
+            
+            payload = {
+                "asin": asin,
+                "title": title,
+                "brand": brand,
+                "link": link
+            }
+            
+            # 2. Ingest into Qdrant using CollectionHandler
+            # Use the injected client 'q' and convert index to int
+            handler = CollectionHandler(
+                collection_name="Product",
+                vector=vector,
+                vector_size=768,
+                payload=payload,
+                id=int(index),
+                link=link,
+                asin=asin,
+                brand=brand,
+                client=q
+            )
+            success = await handler.upsert_point()
+            
+            if success:
+                result_lst.append({
+                    "item": asin, 
+                    "status": "ingested",
+                    "payload": payload
+                })
+                log.info(f"Vector ingested for {asin} (ID: {index})")
+            else:
+                log.error(f"Failed to ingest vector for {asin}")
+        
+        return JSONResponse({"status": "success", "message": f"Ingested {len(result_lst)} items into Product collection", "result": result_lst})
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
 
 @app_router.post("/create")
 async def create_collection_endpoint(

model_export/dino_image_matching.py

@@ -1,9 +1,15 @@
 import warnings
-
+from dotenv import load_dotenv
-import torch
+import torch,glob
 from PIL import Image
 from torchvision import transforms
+import os
+from qdrant_client import AsyncQdrantClient, models
+from db_setup import get_qdrant_client
+from vector_db_router.models import CollectionHandler
+from vector_db_router.serializers import CreateCollectionSerializer
 import torch.nn.functional as F
+load_dotenv()
 
 # Optional dependency warnings from DINOv2 internals are non-critical.
 warnings.filterwarnings("ignore", message="xFormers is not available.*", category=UserWarning)
@@ -42,13 +48,25 @@ def get_embedding(image_path):
 
     return embedding.cpu()
 
-# Example
+def get_vectors(image_input, item):
-emb1 = get_embedding(r"data_images\B0B39FFJHF\03.jpg")
+    try:
-emb2 = get_embedding(r"data_images\B09RWY127Q\03.jpg")
+        base_dir = os.path.join(os.path.dirname(__file__), "downloaded_images")
+        path = image_input
         
-# Cosine similarity
+        # If image_input is not a valid file, try to find one using the item (ASIN)
-similarity = torch.nn.functional.pdist( 
+        if not (path and os.path.isfile(path)):
-    torch.cat([emb1, emb2])
+            # If path is a directory, use it as base_dir
-)
+            search_base = path if (path and os.path.isdir(path)) else base_dir
+            glob_pattern = os.path.join(search_base, item, "*.jpg")
+            jpg_files = glob.glob(glob_pattern)
+            if jpg_files:
+                path = jpg_files[0]
+            else:
+                return None
         
-print("Distance:", similarity.item())
+        # Generate the vector for the identified image file
+        emb = get_embedding(path)
+        return emb.squeeze().tolist()
+    except Exception as e:
+        print(f"Error generating vector for {item}: {e}")
+        return None

req.txt

@@ -0,0 +1,76 @@
+annotated-doc==0.0.4
+annotated-types==0.7.0
+anyio==4.13.0
+asyncmy==0.2.11
+certifi==2026.4.22
+click==8.3.3
+contourpy==1.3.3
+cuda-bindings==13.2.0
+cuda-pathfinder==1.5.4
+cuda-toolkit==13.0.2
+cycler==0.12.1
+et_xmlfile==2.0.0
+fastapi==0.136.1
+filelock==3.29.0
+fonttools==4.62.1
+fsspec==2026.4.0
+greenlet==3.5.0
+grpcio==1.80.0
+h11==0.16.0
+h2==4.3.0
+hpack==4.1.0
+httpcore==1.0.9
+httpx==0.28.1
+hyperframe==6.1.0
+idna==3.13
+Jinja2==3.1.6
+joblib==1.5.3
+kiwisolver==1.5.0
+MarkupSafe==3.0.3
+matplotlib==3.10.9
+mpmath==1.3.0
+networkx==3.6.1
+numpy==2.4.4
+nvidia-cublas==13.1.0.3
+nvidia-cuda-cupti==13.0.85
+nvidia-cuda-nvrtc==13.0.88
+nvidia-cuda-runtime==13.0.96
+nvidia-cudnn-cu13==9.19.0.56
+nvidia-cufft==12.0.0.61
+nvidia-cufile==1.15.1.6
+nvidia-curand==10.4.0.35
+nvidia-cusolver==12.0.4.66
+nvidia-cusparse==12.6.3.3
+nvidia-cusparselt-cu13==0.8.0
+nvidia-nccl-cu13==2.28.9
+nvidia-nvjitlink==13.0.88
+nvidia-nvshmem-cu13==3.4.5
+nvidia-nvtx==13.0.85
+openpyxl==3.1.5
+packaging==26.2
+pandas==3.0.2
+pillow==12.2.0
+portalocker==3.2.0
+protobuf==7.34.1
+pydantic==2.13.4
+pydantic_core==2.46.4
+pyparsing==3.3.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.2.2
+qdrant-client==1.17.1
+scikit-learn==1.8.0
+scipy==1.17.1
+setuptools==81.0.0
+six==1.17.0
+SQLAlchemy==2.0.49
+starlette==1.0.0
+sympy==1.14.0
+threadpoolctl==3.6.0
+torch==2.11.0
+torchvision==0.26.0
+tqdm==4.67.3
+triton==3.6.0
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+urllib3==2.6.3
+uvicorn==0.46.0