adrianoL commited on
Commit
e609dbf
·
verified ·
1 Parent(s): c99568b

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +27 -24
app.py CHANGED
@@ -1,42 +1,45 @@
1
  import gradio as gr
2
  from transformers import pipeline
 
3
 
4
- # Carregando o modelo de QA
5
- QA_modelo = pipeline('question-answering', model='deepset/roberta-base-squad2')
 
 
 
6
 
7
- # Função para obter resposta com o modelo
8
- def obter_resposta(pergunta, contexto):
9
- return QA_modelo(question=pergunta, context=contexto)
 
 
 
 
 
 
 
 
 
 
 
10
 
11
- # Dicionário com perguntas e contextos
12
- contextos = {
13
- "How do I create an account?": "You can create an account by clicking on the 'Sign Up' button on our homepage...",
14
- "Which payment methods do you accept?": "We accept a wide range of payment methods including Visa, MasterCard...",
15
- "How can I track my order?": "Once your order has shipped, you will receive an email with a tracking number...",
16
- "Do you offer international shipping?": "Yes, we offer international shipping to most countries...",
17
- "How long does delivery take?": "For standard shipping, deliveries typically take between 3 to 5 business days...",
18
- "What is your return policy?": "Our return policy allows you to return products within 30 days of receiving them...",
19
- "Can I change or cancel my order after it's been placed?": "You can change or cancel your order within 24 hours...",
20
- "What should I do if I receive a damaged item?": "If you receive a damaged item, please contact our customer service...",
21
- "How do I reset my password?": "If you've forgotten your password, go to the login page and click on 'Forgot Password'..."
22
- }
23
-
24
- # Função para responder perguntas da FAQ
25
  def respondendo_faq(pergunta):
26
- contexto = contextos.get(pergunta) # Verifica se a pergunta existe no dicionário
27
  if not contexto:
28
- return "Pergunta não encontrada. Selecione uma pergunta válida."
 
29
  resultado = obter_resposta(pergunta, contexto)
30
  return resultado['answer']
31
 
32
- # Interface Gradio
33
  app = gr.Interface(
34
  fn=respondendo_faq,
35
  inputs=gr.Dropdown(choices=list(contextos.keys()), label="Select your question"),
36
  outputs=gr.Textbox(label="Answer"),
37
  title='E-commerce FAQ',
38
- description='Select a question to get an answer from our FAQ.'
 
 
39
  )
40
 
41
  if __name__ == "__main__":
42
- app.launch(share=True)
 
1
  import gradio as gr
2
  from transformers import pipeline
3
+ import time
4
 
5
+ try:
6
+ QA_modelo = pipeline('question-answering', model='deepset/roberta-base-squad2')
7
+ except Exception as e:
8
+ print(f"Error loading model: {e}")
9
+ exit(1)
10
 
11
+ def obter_resposta(pergunta, contexto, max_context_length=512):
12
+ try:
13
+ if len(contexto) > max_context_length:
14
+ contexto = contexto[:max_context_length]
15
+
16
+ start_time = time.time()
17
+ resultado = QA_modelo(question=pergunta, context=contexto)
18
+
19
+ if time.time() - start_time > 30:
20
+ return {"answer": "Response timeout. Please try again."}
21
+
22
+ return resultado
23
+ except Exception as e:
24
+ return {"answer": f"Error processing question: {e}"}
25
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
26
  def respondendo_faq(pergunta):
27
+ contexto = contextos.get(pergunta)
28
  if not contexto:
29
+ return "Question not found. Please select a valid question."
30
+
31
  resultado = obter_resposta(pergunta, contexto)
32
  return resultado['answer']
33
 
 
34
  app = gr.Interface(
35
  fn=respondendo_faq,
36
  inputs=gr.Dropdown(choices=list(contextos.keys()), label="Select your question"),
37
  outputs=gr.Textbox(label="Answer"),
38
  title='E-commerce FAQ',
39
+ description='Select a question to get an answer from our FAQ.',
40
+ examples=list(contextos.keys())[:3],
41
+ theme="huggingface"
42
  )
43
 
44
  if __name__ == "__main__":
45
+ app.launch(share=True)