app.py

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

# Carregamento dos modelos
model_a = pipeline("text-generation", model="tiiuae/falcon-7b-instruct")
model_b = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1")
similarity_model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L6-v2")

def comparar_respostas(prompt):
    resp_a = model_a(prompt, max_new_tokens=80)[0]["generated_text"]
    resp_b = model_b(prompt, max_new_tokens=80)[0]["generated_text"]
    emb_a = similarity_model.encode(resp_a, convert_to_tensor=True)
    emb_b = similarity_model.encode(resp_b, convert_to_tensor=True)
    similaridade = util.cos_sim(emb_a, emb_b).item()

    return resp_a.strip(), resp_b.strip(), f"{similaridade:.4f}"

interface = gr.Interface(
    fn=comparar_respostas,
    inputs=gr.Textbox(label="Digite seu prompt"),
    outputs=[
        gr.Textbox(label="Resposta do Modelo A (Falcon)"),
        gr.Textbox(label="Resposta do Modelo B (Mistral)"),
        gr.Textbox(label="Similaridade entre as respostas")
    ],
    title="Comparador de Modelos LLM - Hugging Face"
)

interface.launch()