Update app.py

app.py

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+import gradio as gr
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+import os
+from model.utils import preprocess_input, save_feedback
+from model.auto_learn import trigger_auto_learning
+
+# Carregar modelo e tokenizador
+model_name = "gpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+
+# Mover para GPU se disponível
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Função principal de inferência
+def generate_text(prompt, max_length=100, temperature=0.7):
+    inputs = preprocess_input(prompt, tokenizer)
+    input_ids = inputs["input_ids"].to(device)
+    
+    outputs = model.generate(
+        input_ids,
+        max_length=max_length,
+        temperature=temperature,
+        num_return_sequences=1,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+# Função para coletar feedback e disparar autoaprendizado
+def submit_feedback(prompt, generated_text, user_feedback):
+    save_feedback(prompt, generated_text, user_feedback)
+    trigger_auto_learning()  # Dispara fine-tuning se necessário
+    return "Feedback salvo com sucesso!"
+
+# Interface com Gradio
+def create_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# GPT-2 no Hugging Face")
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(label="Digite seu prompt")
+                max_length = gr.Slider(50, 500, value=100, label="Comprimento máximo")
+                temperature = gr.Slider(0.1, 1.0, value=0.7, label="Temperatura")
+                generate_btn = gr.Button("Gerar Texto")
+            with gr.Column():
+                output = gr.Textbox(label="Texto Gerado")
+                feedback = gr.Textbox(label="Feedback (opcional)")
+                feedback_btn = gr.Button("Enviar Feedback")
+        
+        generate_btn.click(
+            fn=generate_text,
+            inputs=[prompt, max_length, temperature],
+            outputs=output
+        )
+        feedback_btn.click(
+            fn=submit_feedback,
+            inputs=[prompt, output, feedback],
+            outputs=gr.Textbox()
+        )
+    
+    return demo
+
+# Iniciar a interface
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch(server_name="0.0.0.0", server_port=7860)