Update app.py

app.py

@@ -1,43 +1,48 @@
 import gradio as gr
 from transformers import pipeline
-import time
 
+# Carregando o modelo de QA
 try:
     QA_modelo = pipeline('question-answering', model='deepset/roberta-base-squad2')
 except Exception as e:
-    print(f"Error loading model: {e}")
+    print(f"Erro ao carregar o modelo: {e}")
     exit(1)
 
-def obter_resposta(pergunta, contexto, max_context_length=512):
+# Dicionário com perguntas e contextos - MOVIDO PARA ANTES DA CRIAÇÃO DA INTERFACE
+contextos = {
+    "How do I create an account?": "You can create an account by clicking on the 'Sign Up' button on our homepage...",
+    "Which payment methods do you accept?": "We accept a wide range of payment methods including Visa, MasterCard...",
+    "How can I track my order?": "Once your order has shipped, you will receive an email with a tracking number...",
+    "Do you offer international shipping?": "Yes, we offer international shipping to most countries...",
+    "How long does delivery take?": "For standard shipping, deliveries typically take between 3 to 5 business days...",
+    "What is your return policy?": "Our return policy allows you to return products within 30 days of receiving them...",
+    "Can I change or cancel my order after it's been placed?": "You can change or cancel your order within 24 hours...",
+    "What should I do if I receive a damaged item?": "If you receive a damaged item, please contact our customer service...",
+    "How do I reset my password?": "If you've forgotten your password, go to the login page and click on 'Forgot Password'..."
+}
+
+# Função para obter resposta com o modelo
+def obter_resposta(pergunta, contexto):
     try:
-        if len(contexto) > max_context_length:
-            contexto = contexto[:max_context_length]
-        
-        start_time = time.time()
-        resultado = QA_modelo(question=pergunta, context=contexto)
-        
-        if time.time() - start_time > 30:
-            return {"answer": "Response timeout. Please try again."}
-        
-        return resultado
+        return QA_modelo(question=pergunta, context=contexto)
     except Exception as e:
-        return {"answer": f"Error processing question: {e}"}
+        return {"answer": f"Erro ao processar: {e}"}
 
+# Função para responder perguntas da FAQ
 def respondendo_faq(pergunta):
-    contexto = contextos.get(pergunta)
+    contexto = contextos.get(pergunta)  # Verifica se a pergunta existe no dicionário
     if not contexto:
         return "Question not found. Please select a valid question."
-    
     resultado = obter_resposta(pergunta, contexto)
     return resultado['answer']
 
+# Interface Gradio
 app = gr.Interface(
     fn=respondendo_faq,
     inputs=gr.Dropdown(choices=list(contextos.keys()), label="Select your question"),
     outputs=gr.Textbox(label="Answer"),
     title='E-commerce FAQ',
     description='Select a question to get an answer from our FAQ.',
-    examples=list(contextos.keys())[:3],
     theme="huggingface"
 )