from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import gradio as gr

model_name = "google/flan-t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)

# New, more specific instructions
personas = {
    "🟢 Optimist": "Give a detailed, optimistic argument with at least two clear benefits and an example.",
    "🔴 Pessimist": "Give a detailed, critical argument highlighting at least two risks or drawbacks and an example.",
    "🟡 Neutral": "Provide a balanced perspective. Start by listing pros, then cons, and conclude with a neutral summary."
}

def generate_debate(topic):
    results = []
    for label, instruction in personas.items():
        prompt = (
            f"You are an experienced debater.\n"
            f"Debate Topic: \"{topic}\"\n"
            f"{instruction}\n"
            f"Write at least 3–4 sentences."
        )
        response = pipe(
            prompt,
            max_new_tokens=180,  # increased from 120
            temperature=0.7
        )[0]['generated_text'].strip()
        results.append(f"### {label}\n{response}")
    return "\n\n".join(results)

demo = gr.Interface(
    fn=generate_debate,
    inputs=gr.Textbox(label="Debate Topic"),
    outputs=gr.Markdown(),
    title="🎙️ Multi-Agent Debate Simulator",
    description="Debates with Optimist, Pessimist & Neutral perspectives using FLAN-T5-Base."
)

demo.launch()