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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_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)
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, {"prompt_POS": prompt_POS, "vicuna_chatbot1_POS": vicuna_chatbot1_POS})
send_button_Chunk.click(chatbot_interface_Chunk, {"prompt_chunk": prompt_chunk, "vicuna_chatbot1_chunk": vicuna_chatbot1_chunk})
demo.launch()
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