app.py

import gradio as gr
from huggingface_hub import InferenceClient
from sentence_transformers import SentenceTransformer, util
from transformers import pipeline

# Modelos
chat_model_zephyr = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
chat_model_gpt2 = pipeline("text-generation", model="gpt2", max_new_tokens=100)

# Similaridade
similarity_model = SentenceTransformer("all-MiniLM-L6-v2")


def get_zephyr_response(question):
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": question}
    ]
    response = chat_model_zephyr.chat_completion(
        messages,
        max_tokens=256,
        temperature=0.7,
        top_p=0.95,
    )
    return response.choices[0].message.content.strip()


def get_gpt2_response(question):
    generated = chat_model_gpt2(question)[0]["generated_text"]
    return generated.strip()


def compare_answers(answer1, answer2):
    emb1 = similarity_model.encode(answer1, convert_to_tensor=True)
    emb2 = similarity_model.encode(answer2, convert_to_tensor=True)
    similarity = util.cos_sim(emb1, emb2).item()
    return round(similarity, 3)


def respond(question):
    answer_zephyr = get_zephyr_response(question)
    answer_gpt2 = get_gpt2_response(question)
    similarity = compare_answers(answer_zephyr, answer_gpt2)

    return (
        f"🧠 Zephyr-7b:\n{answer_zephyr}\n\n"
        f"🤖 GPT-2:\n{answer_gpt2}\n\n"
        f"🔍 Similaridade Semântica: **{similarity}**"
    )


with gr.Blocks() as demo:
    gr.Markdown("# 🤖 Comparador de Respostas (sem contexto)\nDigite uma pergunta e veja as respostas de dois modelos.")
    question = gr.Textbox(label="Pergunta")
    submit = gr.Button("Comparar Respostas")
    output = gr.Textbox(label="Respostas e Similaridade", lines=15)

    submit.click(respond, inputs=question, outputs=output)

if __name__ == "__main__":
    demo.launch()