mariusjabami commited on
Commit
403e5d7
·
verified ·
1 Parent(s): a174543

Update app.py

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Files changed (1) hide show
  1. app.py +16 -21
app.py CHANGED
@@ -1,19 +1,18 @@
1
  import gradio as gr
 
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  from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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  import threading
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- import torch
5
 
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- # Detectar dispositivo automaticamente (GPU ou CPU)
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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- # Carregar modelo e tokenizer
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  model_name = "lambdaindie/lambda-1v-1B"
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  model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  stop_flag = {"stop": False}
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- # Função de resposta
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  def respond(prompt, history):
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  stop_flag["stop"] = False
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@@ -22,7 +21,6 @@ def respond(prompt, history):
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  streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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- # Iniciar thread de geração
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  generation_thread = threading.Thread(
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  target=model.generate,
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  kwargs={
@@ -43,29 +41,26 @@ def respond(prompt, history):
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  if stop_flag["stop"]:
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  return "", history
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  reasoning += new_text
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- yield "", (history or []) + [(prompt, f"<div class='final-answer'>{reasoning}</div>")]
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- # Função para parar a geração
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  def stop_generation():
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  stop_flag["stop"] = True
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- # Interface Gradio
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  with gr.Blocks(css="""
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- #chatbot, .gr-markdown, .gr-button, .gr-textbox {
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- font-family: 'JetBrains Mono', monospace !important;
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- font-size: 11px !important;
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- }
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- .final-answer {
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- background-color: #1e1e1e;
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- color: #ffffff;
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- padding: 10px;
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- border-left: 4px solid #4caf50;
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- font-family: 'JetBrains Mono', monospace !important;
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- white-space: pre-wrap;
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- font-size: 11px !important;
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  }
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  """) as demo:
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- gr.Markdown('<link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono&display=swap" rel="stylesheet">')
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  gr.Markdown("## λambdAI — Reasoning Chat")
70
 
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  chatbot = gr.Chatbot(elem_id="chatbot")
 
1
  import gradio as gr
2
+ import torch
3
  from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
4
  import threading
 
5
 
6
+ # Detectar dispositivo automaticamente
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
8
 
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+ # Inicializar o modelo e o tokenizer
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  model_name = "lambdaindie/lambda-1v-1B"
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  model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
13
 
14
  stop_flag = {"stop": False}
15
 
 
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  def respond(prompt, history):
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  stop_flag["stop"] = False
18
 
 
21
 
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  streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
23
 
 
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  generation_thread = threading.Thread(
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  target=model.generate,
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  kwargs={
 
41
  if stop_flag["stop"]:
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  return "", history
43
  reasoning += new_text
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+ yield "", history[:-1] + [(prompt, f"<div class='final-answer'>{reasoning}</div>")]
45
 
 
46
  def stop_generation():
47
  stop_flag["stop"] = True
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49
  with gr.Blocks(css="""
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+ #chatbot, .gr-markdown, .gr-button, .gr-textbox {
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+ font-family: 'JetBrains Mono', monospace !important;
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+ font-size: 11px !important;
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+ }
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+ .final-answer {
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+ background-color: #1e1e1e;
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+ color: #ffffff;
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+ padding: 10px;
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+ border-left: 4px solid #4caf50;
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+ font-family: 'JetBrains Mono', monospace !important;
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+ white-space: pre-wrap;
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+ font-size: 11px !important;
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  }
63
  """) as demo:
 
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  gr.Markdown("## λambdAI — Reasoning Chat")
65
 
66
  chatbot = gr.Chatbot(elem_id="chatbot")