Update handler.py

handler.py

@@ -1,28 +1,31 @@
-from typing import Dict, List, Any
-from lmdeploy import pipeline
-from lmdeploy.vl import load_image
-from lmdeploy.messages import TurbomindEngineConfig
-
-class EndpointHandler():
-    def __init__(self, path):
-        # Preload the model at initialization
-        backend_config = TurbomindEngineConfig(model_name ="OpenGVLab/InternVL-Chat-V1-5",model_format='hf',tp=1)
-        self.pipe = pipeline(f"{path}", backend_config=backend_config, log_level='INFO')
-
-    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        data args:
-            inputs (:obj: `str` | `PIL.Image` | `np.array`)
-            kwargs
-        Return:
-            A :obj:`list` | `dict`: will be serialized and returned
-        """
-        image_url = data.get('image_url')
-        if not image_url:
-            return {'error': 'No image URL provided'}
-
-        image = load_image(image_url)
-        prompt = """Please analyze the provided image to determine if it contains any form of analytical representation, such as a chart, graph, or table. If such a representation is present, extract all the information into a textual, relational data format and provide a detailed description of the image. If the image does not contain any analytical representation, give only "none" without any additional words."""
-        
-        response = self.pipe((prompt, image))
-        return {'response': response.text}
+from typing import Dict, List, Any
+from lmdeploy import pipeline
+from lmdeploy.vl import load_image
+from lmdeploy.messages import TurbomindEngineConfig
+
+class EndpointHandler():
+    def __init__(self, path):
+        # Preload the model at initialization
+        backend_config = TurbomindEngineConfig(model_name ="OpenGVLab/InternVL-Chat-V1-5",model_format='hf',tp=1)
+        self.pipe = pipeline(f"{path}", backend_config=backend_config, log_level='INFO')
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+        Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+        try:
+          image_url = data.get('image_url', "")
+          prompt = data.get("prompt", "")
+          if not image_url:
+              return {'error': 'No image URL provided'}
+
+          image = load_image(image_url)
+
+          response = self.pipe((prompt, image))
+          return {'response': response.text}
+        except Exception as err:
+          return {"error", str(err)}