Update app.py

app.py

@@ -66,15 +66,16 @@ def warmup(model_list=model_list, model_inference_endpoints=inference_endpoint):
     return {
         options: gr.update(visible=True),
         inputbox: gr.update(visible=True),
+        submit: gr.update(visible=True)
         warmup_button: gr.update(visible=False),
         welcome_message: gr.update(visible=True)
     }
 
-def inference(model_list, API_KEY, cot):
+def inference(model_list, question, API_KEY, cot):
     if len(model_list) != 3:
         raise gr.Error("Please choose just '3' models! Neither more nor less!")
 
-    model_response = Inference(model_list, API_KEY, cot)
+    model_response = Inference(model_list, question, API_KEY, cot)
 
     return {
         output_msg: gr.update(visible=True),
@@ -137,20 +138,22 @@ TITLE = """<h1 align="center">LLM Agora 🗣️🏦</h1>"""
 
 INTRODUCTION_TEXT = """
 The **LLM Agora** 🗣️🏦 aims to improve the quality of open-source LMs' responses through debate & revision introduced in [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://arxiv.org/abs/2305.14325).
+Thank you to the authors of this paper for suggesting a great idea!
 
 Do you know that? 🤔 **LLMs can also improve their responses by debating with other LLMs**! 😮 We applied this concept to several open-source LMs to verify that the open-source model, not the proprietary one, can sufficiently improve the response through discussion. 🤗
 For more details, please refer to the [GitHub Repository](https://github.com/gauss5930/LLM-Agora).
+You can also check the results in this Space!
 
 You can use LLM Agora with your own questions if the response of open-source LM is not satisfactory and you want to improve the quality!
 The Math, GSM8K, and MMLU Tabs show the results of the experiment(Llama2, WizardLM2, Orca2), and for inference, please use the 'Inference' tab.
 
 Here's how to use LLM Agora!
 
-1. Before start, click the 'Warm-up LLM Agora 🔥' button and wait until 'LLM Agora Ready!!' appears. (Go grab a coffee☕ since it takes 5 minutes!)
+1. Before starting, click the 'Warm-up LLM Agora 🔥' button and wait until 'LLM Agora Ready!!' appears. (Suggest to go grab a coffee☕ since it takes 5 minutes!)
 2. Choose just 3 models! Neither more nor less!
 3. Check the CoT box if you want to utilize the Chain-of-Thought while inferencing.
 4. Please fill in your OpenAI API KEY, it will be used to use ChatGPT to summarize the responses.
-5. Type your question to Question box and click the 'Submit' button! If you do so, LLM Agora will show you improved answers! 🤗 (It will spend roughly a minute! Please wait for an answer!)
+5. Type your question in the Question box and click the 'Submit' button! If you do so, LLM Agora will show you improved answers! 🤗 (It will take roughly a minute! Please wait for an answer!)
 
 For more detailed information, please check '※ Specific information about LLM Agora' at the bottom of the page.
 """
@@ -164,6 +167,8 @@ This is the specific information about LLM Agora!
 
 **Model size**
 
+Besides Falcon, all other models are based on Llama2.
+
 |Model name|Model size|
 |---|---|
 |Llama2|13B|
@@ -176,7 +181,7 @@ This is the specific information about LLM Agora!
 
 **Agent numbers & Debate rounds**
 
-- We limit the number of agents and debate rounds because of limitation of resources. As a result, we decided to use 3 agents and 2 rounds of debate!
+- We limit the number of agents and debate rounds because of the limitation of resources. As a result, we decided to use 3 agents and 2 rounds of debate!
 
 **GitHub Repository**
 
@@ -209,7 +214,7 @@ with gr.Blocks() as demo:
                     API_KEY = gr.Textbox(label="OpenAI API Key", value="", info="Please fill in your OpenAI API token.", placeholder="sk..", type="password")
             with gr.Column(visible=False) as inputbox:
                 question = gr.Textbox(label="Question", value="", info="Please type your question!", placeholder="")
-            submit = gr.Button("Submit")
+            submit = gr.Button("Submit", visible=False)
 
             with gr.Row(visible=False) as output_msg:
                 gr.HTML(RESPONSE_TEXT)
@@ -292,10 +297,22 @@ with gr.Blocks() as demo:
             gr.HTML("""<h1 align="center"> The result of GSM8K </h1>""")
             # gr.Image(value="result/GSM8K/gsm_result.png")
 
+        gsm_cot.select(
+            gsm_display_question_answer,
+            [gsm_question_list, gsm_cot],
+            [gsm_model1_output1, gsm_model2_output1, gsm_model3_output1, gsm_summarization_text1, gsm_model1_output2, gsm_model2_output2, gsm_model3_output2, gsm_summarization_text2, gsm_model1_output3, gsm_model2_output3, gsm_model3_output3]
+        )
+        gsm_question_list.change(
+            gsm_display_question_answer,
+            [gsm_question_list, gsm_cot],
+            [gsm_model1_output1, gsm_model2_output1, gsm_model3_output1, gsm_summarization_text1, gsm_model1_output2, gsm_model2_output2, gsm_model3_output2, gsm_summarization_text2, gsm_model1_output3, gsm_model2_output3, gsm_model3_output3]
+        )
+
 
         with gr.Tab("MMLU"):
             mmlu_cot = gr.Checkbox(label="CoT", info="If you want to see CoT result, please check the box.")
             # mmlu_question_list = gr.Dropdown(mmlu_questions, value=callable(mmlu_questions), label="MMLU Question", every=0.1)
+            
             with gr.Column():
                 with gr.Row(elem_id="model1_response"):
                     mmlu_model1_output1 = gr.Textbox(label="Llama2🦙's 1️⃣st response")
@@ -315,10 +332,21 @@ with gr.Blocks() as demo:
             gr.HTML("""<h1 align="center"> The result of MMLU </h1>""")
             # gr.Image(value="result/MMLU/mmlu_result.png")
 
+        mmlu_cot.select(
+            mmlu_display_question_answer,
+            [mmlu_question_list, mmlu_cot],
+            [mmlu_model1_output1, mmlu_model2_output1, mmlu_model3_output1, mmlu_summarization_text1, mmlu_model1_output2, mmlu_model2_output2, mmlu_model3_output2, mmlu_summarization_text2, mmlu_model1_output3, mmlu_model2_output3, mmlu_model3_output3]
+        )
+        mmlu_question_list.change(
+            mmlu_display_question_answer,
+            [mmlu_question_list, mmlu_cot],
+            [mmlu_model1_output1, mmlu_model2_output1, mmlu_model3_output1, mmlu_summarization_text1, mmlu_model1_output2, mmlu_model2_output2, mmlu_model3_output2, mmlu_summarization_text2, mmlu_model1_output3, mmlu_model2_output3, mmlu_model3_output3]
+        )
+
         with gr.Accordion("※ Specific information about LLM Agora", open=False):
             gr.Markdown(SPECIFIC_INFORMATION)
 
-    warmup_button.click(warmup, [], [options, inputbox, warmup_button, welcome_message])
-    submit.click(inference, [model_list, API_KEY, cot], [output_msg, output_col, model1_output1, model2_output1, model3_output1, summarization_text1, model1_output2, model2_output2, model3_output2, summarization_text2, model1_output3, model2_output3, model3_output3])
+    warmup_button.click(warmup, [], [options, inputbox, submit, warmup_button, welcome_message])
+    submit.click(inference, [model_list, submit, API_KEY, cot], [output_msg, output_col, model1_output1, model2_output1, model3_output1, summarization_text1, model1_output2, model2_output2, model3_output2, summarization_text2, model1_output3, model2_output3, model3_output3])
 
 demo.launch()