- Added python-server folder

python_server/downloadModel.py

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+from transformers import CLIPModel, CLIPProcessor
+
+# pip install torch torchvision torchaudio
+# pip install transformers
+
+# Local folder where you want to save the model
+#local_model_path = "models/clip-vit-base-patch32"
+local_model_path = "models/clip-vit-large-patch14"
+
+# Load model & processor
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+# Save them locally
+model.save_pretrained(local_model_path)
+processor.save_pretrained(local_model_path)
+
+print(f"Model saved to {local_model_path}")
+
+# Then, package model in cmd
+# tar -czvf clip_model.tar.gz mode

python_server/server.py

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+import torch
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+import io
+import json
+from qdrant_client import QdrantClient
+import base64
+import logging
+from fastapi import FastAPI, Request
+import uvicorn
+
+#from pyngrok import ngrok, conf
+
+#conf.get_default().auth_token = "31y03xgLo8cpZ0WujlWkTCT34qK_86cXFLfdH2c3gX3SEezaz"
+
+# ngrok config add-authtoken 31y03xgLo8cpZ0WujlWkTCT34qK_86cXFLfdH2c3gX3SEezaz
+# ngrop http --url=pegasus-working-bison.ngrok-free.app 8000
+
+# Command to run with splunk observability
+# opentelemetry-instrument python server.py
+
+from fastapi.middleware.cors import CORSMiddleware
+
+# ==============================
+# Logging
+# ==============================
+logging.basicConfig(level=logging.INFO)
+
+# ==============================
+# Model & DB initialization
+# ==============================
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+logging.info(f"Using device: {device}")
+
+# Adjust this path for local model folder
+#model_path = "./model/clip-vit-base-patch32"
+model_path = "models/clip-vit-large-patch14"  
+
+
+logging.info("Loading CLIP model...")
+model = CLIPModel.from_pretrained(model_path).to(device)
+processor = CLIPProcessor.from_pretrained(model_path)
+model.eval()
+logging.info("Model loaded.")
+
+# Qdrant connection
+'''qdrant = QdrantClient(
+    url="https://q-vector-db.beprime.mx",
+    port=443,
+    api_key="GJY54XaG0B94DlKAt5IH9T1Ez67u4R7Z",
+    https=True
+)'''
+qdrant = QdrantClient(
+    host="172.21.4.201",
+    port=5050,
+    api_key="GJY54XaG0B94DlKAt5IH9T1Ez67u4R7Z",
+    https=False
+)
+COLLECTION_NAME = "nazan-ssa"
+
+# ==============================
+# Helper functions
+# ==============================
+def get_embeddings(image_bytes: bytes):
+    try:
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        inputs = processor(images=image, return_tensors="pt")
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        with torch.no_grad():
+            embedding = model.get_image_features(**inputs)
+        embedding = embedding / embedding.norm(p=2, dim=-1, keepdim=True)
+        return embedding.cpu().numpy().astype("float32").flatten()
+    except Exception as e:
+        logging.error(f"Error in get_embeddings: {e}")
+        raise
+
+def similarity_search(embedding, top_k=1, threshold=0.7):
+    try:
+        embedding_list = embedding.astype(float).tolist()
+        results = qdrant.search(
+            collection_name=COLLECTION_NAME,
+            query_vector=embedding_list,
+            limit=top_k
+        )
+        points = getattr(results, "points", results)  # v3/v4 compatibility
+        if not points:
+            return {"status": False, "SKU": None}
+        best = points[0]
+        if best.score >= threshold:
+            print({"status": True, "SKU": best.payload.get("sku")}, "threshold:", best.score)
+            return {"status": True, "SKU": best.payload.get("sku")}
+        print({"status": False, "SKU": None}, "threshold:", best.score)
+        return {"status": False, "SKU": None}
+    except Exception as e:
+        logging.error(f"Error in similarity_search: {e}")
+
+# ==============================
+# FastAPI app
+# ==============================
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # or list of domains
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.post("/predict")
+async def predict(request: Request):
+    try:
+        data = await request.json()
+        if "image_bytes" not in data:
+            return {"status": False, "error": "Missing 'image_bytes'"}
+        image_bytes = base64.b64decode(data["image_bytes"])
+        embedding = get_embeddings(image_bytes)
+        result = similarity_search(embedding)
+        return result
+    except Exception as e:
+        logging.error(f"Error in /predict: {e}")
+        return {"status": False, "error": str(e)}
+    
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+
+@app.post("/predictfile")
+async def predict_file(file: UploadFile = File(...)):
+    try:
+        # Read uploaded file as bytes
+        image_bytes = await file.read()
+
+        # Generate embedding
+        embedding = get_embeddings(image_bytes)
+
+        # Run similarity search
+        result = similarity_search(embedding)
+
+        return result#JSONResponse(content=result)
+
+    except Exception as e:
+        logging.error(f"Error in /predictfile: {e}")
+        return JSONResponse(content={"status": False, "error": str(e)})
+
+
+#from pyngrok import ngrokS
+import uvicorn
+import multiprocessing
+
+def run_server():
+    uvicorn.run("server:app", host="0.0.0.0", port=8000)
+
+if __name__ == "__main__":
+    # Open ngrok tunnel
+    #public_url = ngrok.connect(8000).public_url
+    #print("🌍 Public URL:", public_url)
+
+    # Run FastAPI server in a subprocess
+    #p = multiprocessing.Process(target=run_server)
+    #p.start()
+    #p.join()
+    import uvicorn
+    uvicorn.run("server:app", host="0.0.0.0", port=8000)
+

python_server/windmill.py

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+import wmill
+from wmill import S3Object
+import base64
+import json
+import requests
+
+LOCAL_ENDPOINT = "http://127.0.0.1:8000/predict"
+
+def main(input_file: S3Object):
+    # Load image from S3
+    s3_bytes = wmill.load_s3_file(input_file)
+    payload = {"image_bytes": base64.b64encode(s3_bytes).decode("utf-8")}
+
+    print("CALLING LOCAL ENDPOINT")
+
+    response = requests.post(LOCAL_ENDPOINT, json=payload)
+    result = response.json()
+
+    print("RESULT RECEIVED")
+    return result