Upload handler.py

handler.py

@@ -14,19 +14,47 @@ class EndpointHandler():
         self.processor = Blip2Processor.from_pretrained(path)
         self.generate_model = Blip2ForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float16)
         self.generate_model.to(self.device)
+
+        self.feature_model =  Blip2Model.from_pretrained(path, torch_dtype=torch.float16)
+        self.feature_model.to(self.device)
         
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """
-       data args:
+        data args:
             inputs (:obj: `str` | `PIL.Image` | `np.array`)
             kwargs
-      Return:
+        Return:
             A :obj:`list` | `dict`: will be serialized and returned
         """
+        result = {}
         inputs = data.pop("inputs", data)
         image_url = inputs['image_url']
+        if "prompt" in inputs:
+            prompt = inputs["prompt"]
+        else:
+            prompt = None
+        if "extract_feature" in inputs:
+            extract_feature = inputs["extract_feature"]
+        else:
+            extract_feature = False
+
         image = Image.open(requests.get(image_url, stream=True).raw)
         processed_image = self.processor(images=image, return_tensors="pt").to(self.device, torch.float16)
         generated_ids = self.generate_model.generate(**processed_image)
         generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
-        return image_url, generated_text 
+        result["image_caption"] = generated_text
+        if extract_feature:
+            caption_feature = self.feature_model(**processed_image)
+            result["caption_feature"] = caption_feature
+
+        if prompt:
+            prompt_image_processed = self.processor(images=image, text=prompt, return_tensors="pt").to(self.device, torch.float16)
+            generated_ids = self.generate_model.generate(**prompt_image_processed)
+            generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+            result["image_prompt"] = generated_text
+            pass
+            if extract_feature:
+                prompt_feature = self.feature_model(**prompt_image_processed)
+                result["prompt_feature"] = prompt_feature
+
+        return result