app.py

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

# Modelos A e B
generator_a = pipeline("text2text-generation", model="google/flan-t5-base", tokenizer="google/flan-t5-base")
generator_b = pipeline("text2text-generation", model="declare-lab/flan-alpaca-base", tokenizer="declare-lab/flan-alpaca-base")

# Árbitro baseado em similaridade semântica
similarity_model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L6-v2")

def judge(prompt, response_a, response_b):
    emb_prompt = similarity_model.encode(prompt, convert_to_tensor=True)
    emb_a = similarity_model.encode(response_a, convert_to_tensor=True)
    emb_b = similarity_model.encode(response_b, convert_to_tensor=True)
    
    score_a = util.pytorch_cos_sim(emb_prompt, emb_a).item()
    score_b = util.pytorch_cos_sim(emb_prompt, emb_b).item()
    
    return response_a if score_a >= score_b else response_b

def chat(prompt):
    response_a = generator_a(prompt, max_length=100)[0]['generated_text']
    response_b = generator_b(prompt, max_length=100)[0]['generated_text']
    best = judge(prompt, response_a, response_b)
    return f"Resposta A:\n{response_a}\n\nResposta B:\n{response_b}\n\n✅ Melhor Resposta:\n{best}"

iface = gr.Interface(fn=chat, inputs=gr.Textbox(label="Pergunta"), outputs=gr.Textbox(label="Resposta Escolhida"), title="Chatbot em Cascata")
iface.launch()