TEST_4

app.py

@@ -3,80 +3,63 @@ from huggingface_hub import InferenceClient
 from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
 from sentence_transformers import SentenceTransformer, util
 
-# Carregar modelos
+# Modelos
 model_name = "deepset/roberta-base-squad2"
-qa_pipeline = pipeline('question-answering', model=model_name, tokenizer=model_name)
-chat_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-embed_model = SentenceTransformer('all-MiniLM-L6-v2')
+qa_pipeline = pipeline("question-answering", model=model_name, tokenizer=model_name)
 
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-class MultiModelQA:
-    def __init__(self, qa_pipeline, chat_client, embed_model):
-        self.qa_pipeline = qa_pipeline
-        self.chat_client = chat_client
-        self.embed_model = embed_model
+# Modelo para comparação semântica (cosine similarity)
+similarity_model = SentenceTransformer("all-MiniLM-L6-v2")
 
-    def answer_with_qa_model(self, question, context):
-        return self.qa_pipeline({'question': question, 'context': context})['answer']
 
-    def answer_with_chat_model(self, question, system_message, max_tokens, temperature, top_p):
-        messages = [
-            {"role": "system", "content": system_message},
-            {"role": "user", "content": question}
-        ]
-        response = ""
-        for msg in self.chat_client.chat_completion(
-            messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            token = msg.choices[0].delta.content
-            response += token
-        return response
+def get_qa_pipeline_answer(question, context):
+    return qa_pipeline({"question": question, "context": context})["answer"]
 
-    def comparar_semanticamente(self, resp1, resp2):
-        emb1 = self.embed_model.encode(resp1, convert_to_tensor=True)
-        emb2 = self.embed_model.encode(resp2, convert_to_tensor=True)
-        similarity = util.cos_sim(emb1, emb2).item()
-        return similarity
 
+def get_zephyr_answer(question, context):
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": f"Context: {context}\n\nQuestion: {question}"},
+    ]
+    response = client.chat_completion(
+        messages,
+        max_tokens=512,
+        temperature=0.7,
+        top_p=0.95,
+    )
+    return response.choices[0].message.content.strip()
 
-multiqa = MultiModelQA(qa_pipeline, chat_client, embed_model)
 
+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 responder_e_comparar(question, context, system_message, max_tokens, temperature, top_p):
-    qa_resp = multiqa.answer_with_qa_model(question, context)
-    chat_resp = multiqa.answer_with_chat_model(question, system_message, max_tokens, temperature, top_p)
-    similaridade = multiqa.comparar_semanticamente(qa_resp, chat_resp)
 
-    result = f"""### Resposta do modelo QA:
-{qa_resp}
+def respond(question, context):
+    answer1 = get_qa_pipeline_answer(question, context)
+    answer2 = get_zephyr_answer(question, context)
+    similarity_score = compare_answers(answer1, answer2)
 
-### Resposta do modelo Chat:
-{chat_resp}
-
-### Similaridade semântica (coseno): {similaridade:.2%}
-"""
-    return result
+    return (
+        f"📘 Roberta-base-squad2:\n{answer1}\n\n"
+        f"🧠 Zephyr-7b:\n{answer2}\n\n"
+        f"🔍 Similaridade Semântica: **{similarity_score}**"
+    )
 
 
 # Interface Gradio
-demo = gr.Interface(
-    fn=responder_e_comparar,
-    inputs=[
-        gr.Textbox(label="Pergunta"),
-        gr.Textbox(label="Contexto"),
-        gr.Textbox(value="Você é um assistente útil.", label="Mensagem do sistema"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Máximo de tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperatura"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
-    ],
-    outputs=gr.Markdown(),
-    title="Comparador de Respostas de Modelos",
-    description="Compara as respostas de um modelo de QA e um modelo de chat (Zephyr-7B) e calcula a similaridade semântica entre elas."
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# 🔎 Perguntas com dois modelos\nCompare duas respostas e veja a similaridade.")
+    with gr.Row():
+        question = gr.Textbox(label="Pergunta")
+        context = gr.Textbox(label="Contexto")
+    submit_btn = gr.Button("Obter Respostas")
+    output = gr.Textbox(label="Respostas e Similaridade")
+
+    submit_btn.click(respond, inputs=[question, context], outputs=output)
 
 if __name__ == "__main__":
     demo.launch()