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from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
from typing import List, Tuple, Dict
import torch
# Load model and tokenizer
model_name = "AuriLab/gpt-bi-instruct-cesar"
tokenizer_name = "AuriLab/gpt-bi"
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def format_messages(history: List[Tuple[str, str]], system_message: str, user_message: str) -> str:
# Format conversation history into a single string
formatted_prompt = system_message + "\n\n"
for user, assistant in history:
if user:
formatted_prompt += f"User: {user}\n"
if assistant:
formatted_prompt += f"Assistant: {assistant}\n"
formatted_prompt += f"User: {user_message}\nAssistant:"
return formatted_prompt
def respond(message: str, history: List[Tuple[str, str]]) -> str:
system_message = """You are a helpful assistant. Follow these rules:
1. Provide diverse and varied responses
2. Avoid repeating the same words or phrases
3. Use synonyms and alternative expressions
4. Be concise and direct"""
prompt = format_messages(history, system_message, message)
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
# Generate response
with torch.no_grad():
outputs = model.generate(
inputs["input_ids"],
max_new_tokens=200,
temperature=0.7,
top_p=0.85,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the assistant's response
response = response.split("Assistant:")[-1].strip()
return response
# Create the Gradio interface with custom title
demo = gr.ChatInterface(
fn=respond,
title="Demo GPT-BI instruct",
)
if __name__ == "__main__":
demo.launch()
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