app.py

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import gradio as gr

# Load Falcon-7B-Instruct (open and free)
model_name = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Define personas
personas = {
    "🟢 Optimist": "Someone who sees the good and hopeful side of any issue.",
    "🔴 Pessimist": "Someone who focuses on the risks, negatives, or problems in any issue.",
    "🟡 Neutral": "Someone who presents a balanced, unbiased view based on logic."
}

# Prompt template
def build_prompt(topic, style):
    return f"""You are a debater. Take this persona: {style}.
Debate Topic: "{topic}"
Provide a thoughtful and opinionated response with reasoning.
Answer:"""

# Generate responses
def debate(topic):
    results = {}
    for label, persona in personas.items():
        prompt = build_prompt(topic, persona)
        output = pipe(prompt, max_new_tokens=200, temperature=0.9)[0]["generated_text"]
        answer = output.split("Answer:")[-1].strip()
        results[label] = answer
    return results

# Gradio interface
def run_debate(topic):
    responses = debate(topic)
    return "\n\n".join([f"**{k}**:\n{v}" for k, v in responses.items()])

gr.Interface(
    fn=run_debate,
    inputs=gr.Textbox(label="Enter a Debate Topic"),
    outputs=gr.Markdown(),
    title="🎙️ Multi-Agent Debate Simulator",
    description="Simulates multi-perspective debates using Falcon-7B on Hugging Face 🤗."
).launch()