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import gradio as gr
import random
import time
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load Vicuna 7B model and tokenizer
model_name = "lmsys/vicuna-7b-v1.3"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.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 = 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 = gr.Chatbot(label="vicuna-7b", live=True)
llama_chatbot1 = gr.Chatbot(label="llama-7b", live=False)
gpt_chatbot1 = gr.Chatbot(label="gpt-3.5", live=False)
gr.Markdown("Strategy 2 Instruction")
with gr.Row():
vicuna_chatbot2 = gr.Chatbot(label="vicuna-7b", live=True)
llama_chatbot2 = gr.Chatbot(label="llama-7b", live=False)
gpt_chatbot2 = gr.Chatbot(label="gpt-3.5", live=False)
gr.Markdown("Strategy 3 Structured Prompting")
with gr.Row():
vicuna_chatbot3 = gr.Chatbot(label="vicuna-7b", live=True)
llama_chatbot3 = gr.Chatbot(label="llama-7b", live=False)
gpt_chatbot3 = gr.Chatbot(label="gpt-3.5", live=False)
clear = gr.ClearButton([prompt, vicuna_chatbot1])
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)
clear = gr.ClearButton([prompt_chunk, vicuna_chatbot1_chunk])
# Define the function for generating responses
def generate_response(model, tokenizer, prompt):
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=500, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(outputs[0])
return response
# Define the Gradio interface
def chatbot_interface(prompt):
vicuna_response = generate_response(model, tokenizer, prompt)
# llama_response = generate_response(llama_model, llama_tokenizer, prompt)
return {"Vicuna-7B": vicuna_response}
# Replace the old respond function with the new general function for Vicuna
prompt.submit(chatbot_interface, [prompt, vicuna_chatbot1, vicuna_chatbot1_chunk])
demo.launch()
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