Update app.py

app.py

@@ -42,16 +42,20 @@ newmodel = PeftModel.from_pretrained(newmodel, peft_model_id,
                                     use_auth_token="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", load_in_8bit=True, device_map='cpu')
 
 def givetext(input_text,lmodel,ltokenizer):
-  eval_prompt_pt1 = "\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction: Act like a therapist and respond\n\n### Input: "
-  eval_prompt_pt2="\n\n\n### Response:\n"
-  eval_prompt=eval_prompt_pt1+input_text+eval_prompt_pt2
-  print(eval_prompt,"\n\n")
-  model_input = ltokenizer(eval_prompt, return_tensors="pt").to("cpu")
-
-  lmodel.eval()
-  with torch.no_grad():
-    return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=1000)[0], skip_special_tokens=True))
-    #return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))
+    try:
+        eval_prompt_pt1 = "\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction: Act like a therapist and respond\n\n### Input: "
+        eval_prompt_pt2="\n\n\n### Response:\n"
+        eval_prompt=eval_prompt_pt1+input_text+eval_prompt_pt2
+        print(eval_prompt,"\n\n")
+        model_input = ltokenizer(eval_prompt, return_tensors="pt").to("cpu")
+        
+        lmodel.eval()
+        with torch.no_grad():
+            return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=1000)[0], skip_special_tokens=True))
+            #return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))
+    except Exception as error:
+        print("Exception error}".format(error = error))
+        #txt1 = "My name is {fname}, I'm {age}".format(fname = "John", age = 36)
 
 def mental_chat(message, history):
     return givetext(message,newmodel,newtokenizer)