Update app.py

app.py

@@ -6,45 +6,26 @@ import torch
 
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
-    def predict(input, history=[]):
-        
-        new_user_input_ids = input
-        response = openai.Completion.create(
-        model="davinci:ft-placeholder-2022-12-10-04-13-26",
-        prompt=input
-        temperature=0.13,
-        max_tokens=310,
-        top_p=1,
-        frequency_penalty=0.36,
-        presence_penalty=1.25
-        )
-
-    # generate a response 
-        history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+def predict(input, history=[]):
     
-    # convert the tokens to text, and then split the responses into lines
-        response = tokenizer.decode(history[0]).split("<|endoftext|>")
-        response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-        return response, history
-
-
-
-
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
-    
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-    # generate a response 
+    new_user_input_ids = input
     response = openai.Completion.create(
-    model="text-davinci-003",
-    #model="davinci:ft-placeholder:ai-dhd-2022-12-07-10-09-37",
-    prompt= input,
-    temperature=0.09,
-    max_tokens=608,
+    model="davinci:ft-placeholder-2022-12-10-04-13-26",
+    prompt=input
+    temperature=0.13,
+    max_tokens=310,
     top_p=1,
-    frequency_penalty=0,
-    presence_penalty=0).tolist()
+    frequency_penalty=0.36,
+    presence_penalty=1.25
+    )
+
+# generate a response 
+    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+
+# convert the tokens to text, and then split the responses into lines
+    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
+    return response, history
 
 gr.Interface(fn=predict,
              inputs=["text", "state"],