Create handler.py

handler.py

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+from typing import Dict, List, Any
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and processor from path
+        self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
+        self.model = AutoModel.from_pretrained(path, trust_remote_code=True).half().cuda()
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        """
+        Args:
+            data (:dict:):
+                The payload with the text prompt and generation parameters.
+        """
+        # process input
+        inputs = data.pop("inputs", data)
+        history = data.pop("history", None)
+
+        response, new_history = self.model.chat(self.tokenizer, inputs, history)
+
+        return [{"generated_text": response, "history": new_history}]

requirements.txt

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+protobuf>=3.19.5,<3.20.1
+transformers==4.26.1
+icetk
+cpm_kernels
+torch>=1.10
+gradio