test

app.py

@@ -4,10 +4,13 @@ import time
 import os
 import openai
 
-# Load the Vicuna 7B v1.3 LMSys model and tokenizer
-model_name = "lmsys/vicuna-7b-v1.3"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+# Load the Vicuna 7B model and tokenizer
+vicuna_tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.3")
+vicuna_model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.3")
+
+# Load the LLaMA 7b model and tokenizer
+llama_tokenizer = AutoTokenizer.from_pretrained("luodian/llama-7b-hf")
+llama_model = AutoModelForCausalLM.from_pretrained("luodian/llama-7b-hf")
 
 template_single = '''Please output any <{}> in the following sentence one per line without any additional text: "{}"'''
 
@@ -43,7 +46,7 @@ def format_chat_prompt(message, chat_history, max_convo_length):
     prompt = f"{prompt}\nUser: {message}\nAssistant:"
     return prompt
 
-def respond_gpt(tab_name, message, chat_history, max_convo_length = 10):
+def gpt_respond(tab_name, message, chat_history, max_convo_length = 10):
         formatted_prompt = format_chat_prompt(message, chat_history, max_convo_length)
         print('Prompt + Context:')
         print(formatted_prompt)
@@ -52,10 +55,28 @@ def respond_gpt(tab_name, message, chat_history, max_convo_length = 10):
         chat_history.append((message, bot_message))
         return "", chat_history
 
-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)
+def vicuna_respond(message, chat_history):
+    input_ids = vicuna_tokenizer.encode(message, return_tensors="pt")
+    output_ids = vicuna_model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2)
+    bot_message = vicuna_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+        
+    chat_history.append((message, bot_message))
+    time.sleep(2)
+    return "", chat_history
+
+def llama_respond(message, chat_history):
+    input_ids = llama_tokenizer.encode(message, return_tensors="pt")
+    output_ids = llama_model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2)
+    bot_message = llama_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+        
+    chat_history.append((message, bot_message))
+    time.sleep(2)
+    return "", chat_history
+
+def vicuna_respond(message, chat_history):
+    input_ids = vicuna_tokenizer.encode(message, return_tensors="pt")
+    output_ids = vicuna_model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2)
+    bot_message = vicuna_tokenizer.decode(output_ids[0], skip_special_tokens=True)
         
     chat_history.append((message, bot_message))
     time.sleep(2)
@@ -78,68 +99,32 @@ def interface():
             vicuna_S1_chatbot = gr.Chatbot(label="vicuna-7b")
             llama_S1_chatbot = gr.Chatbot(label="llama-7b")
             gpt_S1_chatbot = gr.Chatbot(label="gpt-3.5")
-        clear = gr.ClearButton(components=[textbox_prompt, vicuna_S1_chatbot])
-        gr.Markdown("Strategy 2 Instruction-Based Prompting")
-        with gr.Row():
-            vicuna_S2_chatbot = gr.Chatbot(label="vicuna-7b")
-            llama_S2_chatbot = gr.Chatbot(label="llama-7b")
-            gpt_S2_chatbot = gr.Chatbot(label="gpt-3.5")
-        clear = gr.ClearButton(components=[textbox_prompt, vicuna_S2_chatbot])
-        gr.Markdown("Strategy 3 Structured Prompting")
-        with gr.Row():
-            vicuna_S3_chatbot = gr.Chatbot(label="vicuna-7b")
-            llama_S3_chatbot = gr.Chatbot(label="llama-7b")
-            gpt_S3_chatbot = gr.Chatbot(label="gpt-3.5")
-        clear = gr.ClearButton(components=[textbox_prompt, vicuna_S3_chatbot])
-
-        textbox_prompt.submit(respond, inputs=[textbox_prompt, vicuna_S1_chatbot], outputs=[textbox_prompt, vicuna_S1_chatbot])
-        textbox_prompt.submit(respond, inputs=[textbox_prompt, vicuna_S2_chatbot], outputs=[textbox_prompt, vicuna_S2_chatbot])
-        textbox_prompt.submit(respond, inputs=[textbox_prompt, vicuna_S3_chatbot], outputs=[textbox_prompt, vicuna_S3_chatbot])
+        clear = gr.ClearButton(components=[textbox_prompt, api_key_input, vicuna_S1_chatbot, llama_S1_chatbot, gpt_S1_chatbot])
+        # gr.Markdown("Strategy 2 Instruction-Based Prompting")
+        # with gr.Row():
+        #     vicuna_S2_chatbot = gr.Chatbot(label="vicuna-7b")
+        #     llama_S2_chatbot = gr.Chatbot(label="llama-7b")
+        #     gpt_S2_chatbot = gr.Chatbot(label="gpt-3.5")
+        # clear = gr.ClearButton(components=[textbox_prompt, vicuna_S2_chatbot])
+        # gr.Markdown("Strategy 3 Structured Prompting")
+        # with gr.Row():
+        #     vicuna_S3_chatbot = gr.Chatbot(label="vicuna-7b")
+        #     llama_S3_chatbot = gr.Chatbot(label="llama-7b")
+        #     gpt_S3_chatbot = gr.Chatbot(label="gpt-3.5")
+        # clear = gr.ClearButton(components=[textbox_prompt, vicuna_S3_chatbot])
+
+        textbox_prompt.submit(vicuna_respond, inputs=[textbox_prompt, vicuna_S1_chatbot], outputs=[textbox_prompt, vicuna_S1_chatbot])
+        # textbox_prompt.submit(respond, inputs=[textbox_prompt, vicuna_S2_chatbot], outputs=[textbox_prompt, vicuna_S2_chatbot])
+        # textbox_prompt.submit(respond, inputs=[textbox_prompt, vicuna_S3_chatbot], outputs=[textbox_prompt, vicuna_S3_chatbot])
+
+        textbox_prompt.submit(llama_respond, inputs=[textbox_prompt, llama_S1_chatbot], outputs=[textbox_prompt, llama_S1_chatbot])
 
         api_key_btn.click(update_api_key, inputs=api_key_input)
-        btn.click(respond_gpt, inputs=[tab_name, textbox_prompt, gpt_S1_chatbot], outputs=[tab_name, textbox_prompt, gpt_S1_chatbot])
+        btn.click(gpt_respond, inputs=[tab_name, textbox_prompt, gpt_S1_chatbot], outputs=[tab_name, textbox_prompt, gpt_S1_chatbot])
 
 with gr.Blocks() as demo:
     gr.Markdown("# LLM Evaluator With Linguistic Scrutiny")
 
-    with gr.Tab("Noun"):
-        interface()
+    interface()
     
-    with gr.Tab("Determiner"):
-        gr.Markdown(" Description ")
-
-        prompt_CHUNK = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
-
-        gr.Markdown("Strategy 1 QA")
-        with gr.Row():
-            vicuna_S1_chatbot_CHUNK = gr.Chatbot(label="vicuna-7b")
-            llama_S1_chatbot_CHUNK = gr.Chatbot(label="llama-7b")
-            gpt_S1_chatbot_CHUNK = gr.Chatbot(label="gpt-3.5")
-        clear = gr.ClearButton([prompt_CHUNK, vicuna_S1_chatbot_CHUNK])
-        gr.Markdown("Strategy 2 Instruction")
-        with gr.Row():
-            vicuna_S2_chatbot_CHUNK = gr.Chatbot(label="vicuna-7b")
-            llama_S2_chatbot_CHUNK = gr.Chatbot(label="llama-7b")
-            gpt_S2_chatbot_CHUNK = gr.Chatbot(label="gpt-3.5")
-        clear = gr.ClearButton([prompt_CHUNK, vicuna_S2_chatbot_CHUNK])
-        gr.Markdown("Strategy 3 Structured Prompting")
-        with gr.Row():
-            vicuna_S3_chatbot_CHUNK = gr.Chatbot(label="vicuna-7b")
-            llama_S3_chatbot_CHUNK = gr.Chatbot(label="llama-7b")
-            gpt_S3_chatbot_CHUNK = gr.Chatbot(label="gpt-3.5")
-        clear = gr.ClearButton([prompt_CHUNK, vicuna_S3_chatbot_CHUNK])
-    
-    with gr.Tab("Noun phrase"):
-        interface()
-    with gr.Tab("Verb phrase"):
-        interface()
-    with gr.Tab("Dependent clause"):
-        interface()
-    with gr.Tab("T-units"):
-        interface()
-
-    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])
-    prompt_CHUNK.submit(respond, [prompt_CHUNK, vicuna_S3_chatbot_CHUNK], [prompt_CHUNK, vicuna_S3_chatbot_CHUNK])
-
 demo.launch()