Update pipeline.py

pipeline.py

@@ -42,11 +42,11 @@ class PreTrainedPipeline():
             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"})
+        inputs = data["inputs"]
+        parameters = {"mode": "image"}
         
         # decode base64 image to PIL
-        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
+        image = Image.open(BytesIO(base64.b64decode(inputs)))
         image = self.transform(image).unsqueeze(0).to(device)   
         text=""
         with torch.no_grad():