TEST_9 (#10)

- TEST_9 (6a414744603814b04da141a6cd72bb606a373478)

app.py

@@ -2,58 +2,29 @@ import gradio as gr
 from transformers import pipeline
 from sentence_transformers import SentenceTransformer, util
 
-class ModelComparator:
-    def __init__(self):
-        self.qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
-        self.text_gen_pipeline = pipeline("text-generation", model="gpt2", max_new_tokens=20)  # menor geração
-        self.sim_model = SentenceTransformer("all-MiniLM-L6-v2")
-
-    def get_qa_answer(self, question, context=None):
-        if not context:
-            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']
-            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(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)
-
-        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}")
-
-model_comparator = ModelComparator()
-
-with gr.Blocks() as demo:
-    gr.Markdown("## Comparador rápido para Hugging Face Spaces")
-    question_input = gr.Textbox(label="Pergunta")
-    context_input = gr.Textbox(label="Contexto para o modelo de QA (opcional)", lines=3)
-    output = gr.Textbox(label="Respostas e Similaridade", lines=15)
-    btn = gr.Button("Comparar")
-
-    btn.click(
-        fn=model_comparator.respond,
-        inputs=[question_input, context_input],
-        outputs=output
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+# 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()