Delete pipeline.py

pipeline.py

@@ -1,56 +0,0 @@
-# https://huggingface.co/florentgbelidji/blip_image_embeddings/raw/main/pipeline.py
-from typing import  Dict, List, Any
-from PIL import Image
-import requests
-import torch
-import base64
-import os
-from io import BytesIO
-from models.blip_feature_extractor import blip_feature_extractor
-from torchvision import transforms
-from torchvision.transforms.functional import InterpolationMode
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-class PreTrainedPipeline():
-    def __init__(self, path=""):
-        # load the optimized model
-        self.model_path = os.path.join(path,'model_large_retrieval_coco.pth') 
-        self.model = blip_feature_extractor(
-            pretrained=self.model_path, 
-            image_size=384, 
-            vit='large',
-            med_config=os.path.join(path, 'configs/med_config.json')
-        )
-        self.model.eval()
-        self.model = self.model.to(device)
-        
-        image_size = 384
-        self.transform = transforms.Compose([
-            transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
-            transforms.ToTensor(),
-            transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-            ]) 
-     
-
-
-    def __call__(self, data: Any) -> Dict[str, List[float]]:
-        """
-        Args:
-            data (:obj:):
-                includes the input data and the parameters for the inference.
-        Return:
-            A :obj:`dict`:. The object returned should be a dict like {"feature_vector": [0.6331314444541931,0.8802216053009033,...,-0.7866355180740356,]} containing :
-                - "feature_vector": A list of floats corresponding to the image embedding.
-        """
-        inputs = data.pop("inputs", data)
-        parameters = data.pop("parameters", {"mode": "image"})
-        
-        # decode base64 image to PIL
-        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
-        image = self.transform(image).unsqueeze(0).to(device)   
-        text=""
-        with torch.no_grad():
-            feature_vector = self.model(image, text, mode=parameters["mode"])[0,0].tolist()
-        # postprocess the prediction
-        return {"feature_vector": feature_vector}