test

app.py

@@ -1,89 +1,25 @@
 import gradio as gr
-import random
-import time
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-# Load Vicuna 7B model and tokenizer
+# Load the Vicuna 7B v1.3 LMSys model and tokenizer
 model_name = "lmsys/vicuna-7b-v1.3"
-model = AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 
 with gr.Blocks() as demo:
-    gr.Markdown("# LLM Evaluator With Linguistic Scrutiny")
-
-    with gr.Tab("POS"):
-        gr.Markdown(" Description ")
-
-        with gr.Row():
-            prompt_POS = gr.Textbox(show_label=False, placeholder="Enter prompt")
-            send_button_POS = gr.Button("Send", scale=0)
-
-        gr.Markdown("Strategy 1 QA")
-        with gr.Row():
-            vicuna_chatbot1_POS = gr.Chatbot(label="vicuna-7b", live=True)
-            llama_chatbot1_POS = gr.Chatbot(label="llama-7b", live=False)
-            gpt_chatbot1_POS = gr.Chatbot(label="gpt-3.5", live=False)
-        gr.Markdown("Strategy 2 Instruction")
-        with gr.Row():
-            vicuna_chatbot2_POS = gr.Chatbot(label="vicuna-7b", live=True)
-            llama_chatbot2_POS = gr.Chatbot(label="llama-7b", live=False)
-            gpt_chatbot2_POS = gr.Chatbot(label="gpt-3.5", live=False)
-        gr.Markdown("Strategy 3 Structured Prompting")
-        with gr.Row():
-            vicuna_chatbot3_POS = gr.Chatbot(label="vicuna-7b", live=True)
-            llama_chatbot3_POS = gr.Chatbot(label="llama-7b", live=False)
-            gpt_chatbot3_POS = gr.Chatbot(label="gpt-3.5", live=False)
-        
-    with gr.Tab("Chunk"):
-        gr.Markdown(" Description 2 ")
-
-        with gr.Row():
-            prompt_chunk = gr.Textbox(show_label=False, placeholder="Enter prompt")
-            send_button_Chunk = gr.Button("Send", scale=0)
-
-        gr.Markdown("Strategy 1 QA")
-        with gr.Row():
-            vicuna_chatbot1_chunk = gr.Chatbot(label="vicuna-7b", live=True)
-            llama_chatbot1_chunk = gr.Chatbot(label="llama-7b", live=False)
-            gpt_chatbot1_chunk = gr.Chatbot(label="gpt-3.5", live=False)
-        gr.Markdown("Strategy 2 Instruction")
-        with gr.Row():
-            vicuna_chatbot2_chunk = gr.Chatbot(label="vicuna-7b", live=True)
-            llama_chatbot2_chunk = gr.Chatbot(label="llama-7b", live=False)
-            gpt_chatbot2_chunk = gr.Chatbot(label="gpt-3.5", live=False)
-        gr.Markdown("Strategy 3 Structured Prompting")
-        with gr.Row():
-            vicuna_chatbot3_chunk = gr.Chatbot(label="vicuna-7b", live=True)
-            llama_chatbot3_chunk = gr.Chatbot(label="llama-7b", live=False)
-            gpt_chatbot3_chunk = gr.Chatbot(label="gpt-3.5", live=False)
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg, chatbot])
+
+    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)
         
-        clear = gr.ClearButton([prompt_chunk, vicuna_chatbot1_chunk])
-
-    # Define the function for generating responses
-    def generate_response(prompt):
-        input_ids = tokenizer.encode(prompt, return_tensors="pt")
-        output_ids = model.generate(input_ids, max_length=500, pad_token_id=tokenizer.eos_token_id)
-        response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-        return response
-
-    # Define the Gradio interface
-    def chatbot_interface_POS(input_dict):
-        prompt_POS = input_dict["prompt_POS"]
-        vicuna_response_POS = generate_response(prompt_POS)
-        # Add responses from other chatbots if needed
-        return {"Vicuna-7B": vicuna_response_POS}
-
-    def chatbot_interface_Chunk(input_dict):
-        prompt_chunk = input_dict["prompt_chunk"]
-        vicuna_response_chunk = generate_response(prompt_chunk)
-        # Add responses from other chatbots if needed
-        return {"Vicuna-7B": vicuna_response_chunk}
-
-    # Connect the interfaces to the functions
-    send_button_POS.click(chatbot_interface_POS, inputs=[prompt_POS, vicuna_chatbot1_POS])
-    send_button_Chunk.click(chatbot_interface_Chunk, inputs=[prompt_chunk, vicuna_chatbot1_chunk])
-
-
+        chat_history.append((message, bot_message))
+        time.sleep(2)
+        return "", chat_history
 
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
 
 demo.launch()