app.py

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load same or different models for each agent
tokenizer1 = AutoTokenizer.from_pretrained("gpt2")
model1 = AutoModelForCausalLM.from_pretrained("gpt2")

tokenizer2 = AutoTokenizer.from_pretrained("gpt2-medium")
model2 = AutoModelForCausalLM.from_pretrained("gpt2-medium")

tokenizer3 = AutoTokenizer.from_pretrained("gpt2-large")
model3 = AutoModelForCausalLM.from_pretrained("gpt2-large")

device = "cuda" if torch.cuda.is_available() else "cpu"
model1, model2, model3 = model1.to(device), model2.to(device), model3.to(device)

def generate_response(model, tokenizer, prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    outputs = model.generate(inputs["input_ids"], max_length=100, pad_token_id=tokenizer.eos_token_id)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

import gradio as gr

def multi_agent_chat(user_input):
    res1 = generate_response(model1, tokenizer1, user_input)
    res2 = generate_response(model2, tokenizer2, user_input)
    res3 = generate_response(model3, tokenizer3, user_input)
    
    return res1, res2, res3

interface = gr.Interface(
    fn=multi_agent_chat,
    inputs=gr.Textbox(lines=2, placeholder="Ask something..."),
    outputs=[
        gr.Textbox(label="Agent 1 (GPT-2)"),
        gr.Textbox(label="Agent 2 (GPT-2 Medium)"),
        gr.Textbox(label="Agent 3 (GPT-2 Large)")
    ],
    title="3-Agent AI Chatbot"
)

interface.launch()