test data

handler.py

@@ -8,76 +8,77 @@ import requests
 
 class EndpointHandler:
     def __init__(self):
-        self.processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
+        pass
+        # self.processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
 
     
-        device = 'gpu' if torch.cuda.is_available() else 'cpu'
-
-        model = LlavaNextForConditionalGeneration.from_pretrained(
-            "llava-hf/llava-v1.6-mistral-7b-hf", 
-            torch_dtype=torch.float32 if device == 'cpu' else torch.float16, 
-            low_cpu_mem_usage=True
-        )
-        model.to(device)
-
-        self.model = model
-        self.device = device
-
-    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """
-        data args:
-            inputs (:obj: `dict`)
-        Return:
-            A :obj:`list` | `dict`: will be serialized and returned
-        """
+        # device = 'gpu' if torch.cuda.is_available() else 'cpu'
 
+        # model = LlavaNextForConditionalGeneration.from_pretrained(
+        #     "llava-hf/llava-v1.6-mistral-7b-hf", 
+        #     torch_dtype=torch.float32 if device == 'cpu' else torch.float16, 
+        #     low_cpu_mem_usage=True
+        # )
+        # model.to(device)
+
+        # self.model = model
+        # self.device = device
+
+    def __call__(self, data):
         return data
-    
-        # get inputs
-        inputs = data.get("inputs")
 
-        if not inputs:
-            return f"Inputs not in payload got {data}"
+    # def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+    #     """
+    #     data args:
+    #         inputs (:obj: `dict`)
+    #     Return:
+    #         A :obj:`list` | `dict`: will be serialized and returned
+    #     """    
+    #     # get inputs
+    #     inputs = data.get("inputs")
+
+    #     if not inputs:
+    #         return f"Inputs not in payload got {data}"
         
-        # get additional date field0
-        prompt = inputs.get("prompt")
-        image_url = inputs.get("image")
+    #     # get additional date field0
+    #     prompt = inputs.get("prompt")
+    #     image_url = inputs.get("image")
 
-        if image_url is None:
-            return "You need to upload an image URL for LLaVA to work."
+    #     if image_url is None:
+    #         return "You need to upload an image URL for LLaVA to work."
 
-        if prompt is None:
-            prompt = "Can you describe this picture focusing on specifics visual artifacts and ambiance (objects, colors, person, athmosphere..). Please stay concise only output keywords and concepts detected."
+    #     if prompt is None:
+    #         prompt = "Can you describe this picture focusing on specifics visual artifacts and ambiance (objects, colors, person, athmosphere..). Please stay concise only output keywords and concepts detected."
         
-        if not self.model:
-            return "Model was not initialized"
+    #     if not self.model:
+    #         return "Model was not initialized"
         
-        if not self.processor:
-            return "Processor was not initialized"
+    #     if not self.processor:
+    #         return "Processor was not initialized"
         
-        # Create a temporary directory
-        with TemporaryDirectory() as tmpdirname:
-            # Download the image
-            response = requests.get(image_url)
-            if response.status_code != 200:
-                return "Failed to download the image."
+    #     # Create a temporary directory
+    #     with TemporaryDirectory() as tmpdirname:
+    #         # Download the image
+    #         response = requests.get(image_url)
+    #         if response.status_code != 200:
+    #             return "Failed to download the image."
 
-            # Define the path for the downloaded image
-            image_path = f"{tmpdirname}/image.jpg"
-            with open(image_path, "wb") as f:
-                f.write(response.content)
+    #         # Define the path for the downloaded image
+    #         image_path = f"{tmpdirname}/image.jpg"
+    #         with open(image_path, "wb") as f:
+    #             f.write(response.content)
 
-            # Open the downloaded image
-            with Image.open(image_path).convert("RGB") as image:
-                prompt = f"[INST] <image>\n{prompt} [/INST]"
+    #         # Open the downloaded image
+    #         with Image.open(image_path).convert("RGB") as image:
+    #             prompt = f"[INST] <image>\n{prompt} [/INST]"
 
-                inputs = self.processor(prompt, image, return_tensors="pt").to(self.device)
+    #             inputs = self.processor(prompt, image, return_tensors="pt").to(self.device)
 
-                output = self.model.generate(**inputs, max_new_tokens=100)
+    #             output = self.model.generate(**inputs, max_new_tokens=100)
 
-                if not output:
-                    return 'Model failed to generate'
+    #             if not output:
+    #                 return 'Model failed to generate'
 
-                clean = self.processor.decode(output[0], skip_special_tokens=True)
+    #             clean = self.processor.decode(output[0], skip_special_tokens=True)
 
-                return clean
+    #             return clean

requirements.txt

@@ -3,4 +3,5 @@ git+https://github.com/huggingface/transformers.git
 spaces
 pillow
 accelerate
-requests
+requests
+holidays