test

app.py

@@ -12,6 +12,20 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
 
 template_single = '''Please output any <{}> in the following sentence one per line without any additional text: "{}"'''
 
+def gpt3(prompt):
+    response = openai.ChatCompletion.create(
+        model='gpt3.5', messages=[{"role": "user", "content": prompt}])
+    return response['choices'][0]['message']['content']
+
+def respond(message, chat_history):
+    input_ids = tokenizer.encode(message, return_tensors="pt")
+    output_ids = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2)
+    bot_message = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+        
+    chat_history.append((message, bot_message))
+    time.sleep(2)
+    return "", chat_history
+
 def interface():
         gr.Markdown(" Description ")
 
@@ -80,20 +94,6 @@ with gr.Blocks() as demo:
         interface()
     with gr.Tab("T-units"):
         interface()
-    
-    def gpt3(prompt):
-        response = openai.ChatCompletion.create(
-            model='gpt3.5', messages=[{"role": "user", "content": prompt}])
-        return response['choices'][0]['message']['content']
-
-    def respond(message, chat_history):
-        input_ids = tokenizer.encode(message, return_tensors="pt")
-        output_ids = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2)
-        bot_message = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-        
-        chat_history.append((message, bot_message))
-        time.sleep(2)
-        return "", chat_history
 
     prompt_CHUNK.submit(respond, [prompt_CHUNK, vicuna_S1_chatbot_CHUNK], [prompt_CHUNK, vicuna_S1_chatbot_CHUNK])
     prompt_CHUNK.submit(respond, [prompt_CHUNK, vicuna_S2_chatbot_CHUNK], [prompt_CHUNK, vicuna_S2_chatbot_CHUNK])