Update handler.py (#1)

- Update handler.py (32958b5317300ea7ce493f656d75945c5f350421)

handler.py

@@ -10,46 +10,49 @@ class EndpointHandler():
         self.model = torch.load(f"{path}/torch_model.pt")
 
     def __call__(self, data):
-        request_inputs = data.pop("inputs", data)
-        template = request_inputs["template"]
-        messages = request_inputs["messages"]
-        char_name = request_inputs["char_name"]
-        user_name = request_inputs["user_name"]
-        template = open(f"{template}.txt", "r").read()
-        user_input = "\n".join([
-            "{name}: {message}".format(
-                name = char_name if (id["role"] == "AI") else user_name,
-                message = id["message"].strip()
-            ) for id in messages
-        ])
-        prompt = template.format(
-            char_name = char_name,
-            user_name = user_name,
-            user_input = user_input
-        )
-        input_ids = self.tokenizer(
-            prompt + f"\n{char_name}:",
-            return_tensors = "pt"
-        ).to("cuda")
-        encoded_output = self.model.generate(
-            input_ids["input_ids"],
-            max_new_tokens = 50,
-            temperature = 0.5,
-            top_p = 0.9,
-            top_k = 0,
-            repetition_penalty = 1.1,
-            pad_token_id = 50256,
-            num_return_sequences = 1
-        )
-        decoded_output = self.tokenizer.decode(encoded_output[0], skip_special_tokens=True).replace(prompt,"")
-        decoded_output = decoded_output.split(f"{char_name}:", 1)[1].split(f"{user_name}:",1)[0].strip()
-        parsed_result = re.sub('\*.*?\*', '', decoded_output).strip()
-        if len(parsed_result) != 0: decoded_output = parsed_result
-        decoded_output = " ".join(decoded_output.replace("*","").split())
         try:
-            parsed_result = decoded_output[:[m.start() for m in re.finditer(r'[.!?]', decoded_output)][-1]+1]
+            request_inputs = data.pop("inputs", data)
+            template = request_inputs["template"]
+            messages = request_inputs["messages"]
+            char_name = request_inputs["char_name"]
+            user_name = request_inputs["user_name"]
+            template = open(f"{template}.txt", "r").read()
+            user_input = "\n".join([
+                "{name}: {message}".format(
+                    name = char_name if (id["role"] == "AI") else user_name,
+                    message = id["message"].strip()
+                ) for id in messages
+            ])
+            prompt = template.format(
+                char_name = char_name,
+                user_name = user_name,
+                user_input = user_input
+            )
+            input_ids = self.tokenizer(
+                prompt + f"\n{char_name}:",
+                return_tensors = "pt"
+            ).to("cuda")
+            encoded_output = self.model.generate(
+                input_ids["input_ids"],
+                max_new_tokens = 50,
+                temperature = 0.5,
+                top_p = 0.9,
+                top_k = 0,
+                repetition_penalty = 1.1,
+                pad_token_id = 50256,
+                num_return_sequences = 1
+            )
+            decoded_output = self.tokenizer.decode(encoded_output[0], skip_special_tokens=True).replace(prompt,"")
+            decoded_output = decoded_output.split(f"{char_name}:", 1)[1].split(f"{user_name}:",1)[0].strip()
+            parsed_result = re.sub('\*.*?\*', '', decoded_output).strip()
             if len(parsed_result) != 0: decoded_output = parsed_result
-        except Exception: pass
+            decoded_output = " ".join(decoded_output.replace("*","").split())
+            try:
+                parsed_result = decoded_output[:[m.start() for m in re.finditer(r'[.!?]', decoded_output)][-1]+1]
+                if len(parsed_result) != 0: decoded_output = parsed_result
+            except Exception: pass
+        except Exception as e:
+            decoded_output = str(e)
         return {
             "role": "AI",
             "message": decoded_output