VietCat commited on
Commit
2002155
·
1 Parent(s): daa84b4

fix request error

Browse files
Files changed (2) hide show
  1. Dockerfile +1 -0
  2. app.py +2 -7
Dockerfile CHANGED
@@ -14,6 +14,7 @@ RUN mkdir -p /app/cache
14
  # Set env vars to avoid permission issues and suppress deprecation warnings
15
  ENV HF_HOME=/app/cache \
16
  PYTHONUNBUFFERED=1
 
17
 
18
  EXPOSE 7860
19
 
 
14
  # Set env vars to avoid permission issues and suppress deprecation warnings
15
  ENV HF_HOME=/app/cache \
16
  PYTHONUNBUFFERED=1
17
+ # ENV HF_HOME=/tmp/.cache
18
 
19
  EXPOSE 7860
20
 
app.py CHANGED
@@ -1,5 +1,4 @@
1
  import os
2
- # Thiết lập biến môi trường HF_HOME
3
  os.environ["HF_HOME"] = "/tmp/hf_home"
4
 
5
  from transformers import GPT2LMHeadModel, GPT2Tokenizer
@@ -7,23 +6,18 @@ import torch
7
  import gradio as gr
8
 
9
  model_id = "NlpHUST/gpt2-vietnamese"
10
-
11
- # Load model and tokenizer
12
  tokenizer = GPT2Tokenizer.from_pretrained(model_id)
13
  model = GPT2LMHeadModel.from_pretrained(model_id)
14
 
15
- # Set to eval mode and use GPU if available
16
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
17
  model.to(device)
18
  model.eval()
19
 
20
- # Inference function
21
  def generate_text(prompt, max_length=100, temperature=1.0):
22
  inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
23
  outputs = model.generate(inputs, max_length=max_length, temperature=temperature, do_sample=True)
24
  return tokenizer.decode(outputs[0], skip_special_tokens=True)
25
 
26
- # Gradio interface
27
  demo = gr.Interface(
28
  fn=generate_text,
29
  inputs=[
@@ -37,4 +31,5 @@ demo = gr.Interface(
37
  allow_flagging="never"
38
  )
39
 
40
- demo.launch(server_name="0.0.0.0", server_port=7860)
 
 
1
  import os
 
2
  os.environ["HF_HOME"] = "/tmp/hf_home"
3
 
4
  from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
6
  import gradio as gr
7
 
8
  model_id = "NlpHUST/gpt2-vietnamese"
 
 
9
  tokenizer = GPT2Tokenizer.from_pretrained(model_id)
10
  model = GPT2LMHeadModel.from_pretrained(model_id)
11
 
 
12
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
13
  model.to(device)
14
  model.eval()
15
 
 
16
  def generate_text(prompt, max_length=100, temperature=1.0):
17
  inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
18
  outputs = model.generate(inputs, max_length=max_length, temperature=temperature, do_sample=True)
19
  return tokenizer.decode(outputs[0], skip_special_tokens=True)
20
 
 
21
  demo = gr.Interface(
22
  fn=generate_text,
23
  inputs=[
 
31
  allow_flagging="never"
32
  )
33
 
34
+ # Đây là yêu cầu quan trọng với Hugging Face Spaces
35
+ app = demo