app.py

import gradio as gr
from transformers import pipeline

# Initialize the text-generation pipeline
pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype="auto", device_map="auto")

# Define agent roles
agents = {
    "Pirate": "You are a friendly chatbot who always responds in the style of a pirate.",
    "Professor": "You are a knowledgeable professor who explains concepts in detail.",
    "Comedian": "You are a witty comedian who answers with humor and jokes.",
    "Motivator": "You are a motivational speaker who provides inspiring and uplifting responses.",
}

def multi_agent_system(agent, user_input):
    # Set the role of the selected agent
    messages = [
        {"role": "system", "content": agents[agent]},
        {"role": "user", "content": user_input},
    ]
    # Format the chat template
    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    # Generate the response
    outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
    return outputs[0]["generated_text"]

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# Multi-Agent Chat System")
    
    with gr.Row():
        agent_dropdown = gr.Dropdown(
            choices=list(agents.keys()), label="Select an Agent", value="Pirate"
        )
        user_input = gr.Textbox(label="Enter your message:", placeholder="Type your query here...")
    
    submit_button = gr.Button("Submit")
    chat_output = gr.Textbox(label="Agent's Response:", interactive=False)
    
    submit_button.click(
        fn=multi_agent_system, 
        inputs=[agent_dropdown, user_input], 
        outputs=chat_output
    )

# Launch the app
demo.launch()