TEST_7 (#8)

- TEST_7 (87426422350dfd50b0941f55fbe860bacb7cfbda)

app.py

@@ -1,61 +1,66 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-from sentence_transformers import SentenceTransformer, util
 from transformers import pipeline
+from sentence_transformers import SentenceTransformer, util
 
-# 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()
-
+class ModelComparator:
+    def __init__(self):
+        # Modelo de QA (mais rápido e leve)
+        self.qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+        # Modelo de geração de texto simples
+        self.text_gen_pipeline = pipeline("text-generation", model="gpt2", max_new_tokens=50)
+        # Modelo para embeddings e similaridade
+        self.sim_model = SentenceTransformer("all-MiniLM-L6-v2")
 
-def get_gpt2_response(question):
-    generated = chat_model_gpt2(question)[0]["generated_text"]
-    return generated.strip()
+    def get_qa_answer(self, question, context=None):
+        # Se não passar contexto, responde "não sei"
+        if context is None:
+            return "No context provided for QA model."
+        try:
+            result = self.qa_pipeline(question=question, context=context)
+            return result['answer']
+        except Exception as e:
+            return f"Error in QA pipeline: {e}"
 
+    def get_text_gen_answer(self, prompt):
+        try:
+            generated = self.text_gen_pipeline(prompt)[0]['generated_text']
+            # O GPT2 gera o texto incluindo o prompt, vamos remover o prompt para deixar só resposta
+            answer = generated[len(prompt):].strip()
+            return answer if answer else generated.strip()
+        except Exception as e:
+            return f"Error in text generation pipeline: {e}"
 
-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 compare_answers(self, answer1, answer2):
+        emb1 = self.sim_model.encode(answer1, convert_to_tensor=True)
+        emb2 = self.sim_model.encode(answer2, convert_to_tensor=True)
+        similarity = util.cos_sim(emb1, emb2).item()
+        return round(similarity, 3)
 
+    def respond(self, question, context):
+        qa_answer = self.get_qa_answer(question, context)
+        gen_answer = self.get_text_gen_answer(question)
+        similarity = self.compare_answers(qa_answer, gen_answer)
 
-def respond(question):
-    answer_zephyr = get_zephyr_response(question)
-    answer_gpt2 = get_gpt2_response(question)
-    similarity = compare_answers(answer_zephyr, answer_gpt2)
+        return (f"Model QA answer:\n{qa_answer}\n\n"
+                f"Model GPT-2 generated answer:\n{gen_answer}\n\n"
+                f"Semantic similarity score: {similarity}")
 
-    return (
-        f"🧠 Zephyr-7b:\n{answer_zephyr}\n\n"
-        f"🤖 GPT-2:\n{answer_gpt2}\n\n"
-        f"🔍 Similaridade Semântica: **{similarity}**"
-    )
+# Interface Gradio
 
+model_comparator = ModelComparator()
 
 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")
+    gr.Markdown("## Comparador de respostas entre dois modelos locais (CPU)")
+    question_input = gr.Textbox(label="Pergunta")
+    context_input = gr.Textbox(label="Contexto para o modelo de QA (opcional)", lines=5)
     output = gr.Textbox(label="Respostas e Similaridade", lines=15)
+    btn = gr.Button("Comparar")
 
-    submit.click(respond, inputs=question, outputs=output)
+    btn.click(
+        fn=model_comparator.respond,
+        inputs=[question_input, context_input],
+        outputs=output
+    )
 
 if __name__ == "__main__":
     demo.launch()