Ivan000 commited on
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19ced57
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1 Parent(s): c0ff2ab

Update app.py

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Files changed (1) hide show
  1. app.py +21 -6
app.py CHANGED
@@ -1,6 +1,6 @@
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  # app.py
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  # =============
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- # This is a complete app.py file for deploying the MTSAIR/Cotype-Nano model using Gradio and Hugging Face Transformers with chat and token streaming functionality.
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  import gradio as gr
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  from transformers import pipeline
@@ -10,17 +10,21 @@ model_name = "MTSAIR/Cotype-Nano"
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  pipe = pipeline("text-generation", model=model_name, device="cpu")
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  # Define the system prompt
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- system_prompt = {"role": "system", "content": "Ты ИИ-помощник. Тебе дано задание: необходимо сгенерировать подробный и развернутый ответ."}
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  # Define the Gradio interface
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- def generate_response(history, user_input):
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  messages = [system_prompt] + history + [{"role": "user", "content": user_input}]
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- response = pipe(messages, max_length=1024, return_full_text=False)
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  generated_text = response[0]['generated_text']
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  history.append({"role": "user", "content": user_input})
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  history.append({"role": "assistant", "content": generated_text})
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  return history, ""
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  # Create the Gradio interface
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  with gr.Blocks() as demo:
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  gr.Markdown("## Cotype-Nano Text Generation Chat")
@@ -30,10 +34,21 @@ with gr.Blocks() as demo:
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  with gr.Row():
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  txt = gr.Textbox(
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  show_label=False,
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- placeholder="Введите ваш запрос здесь...",
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  )
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- txt.submit(generate_response, [chatbot, txt], [chatbot, txt])
 
 
 
 
 
 
 
 
 
 
 
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  # Launch the interface
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  if __name__ == "__main__":
 
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  # app.py
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  # =============
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+ # This is a complete app.py file for deploying the MTSAIR/Cotype-Nano model using Gradio and Hugging Face Transformers with chat and token streaming functionality, advanced settings, and English interface.
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  import gradio as gr
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  from transformers import pipeline
 
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  pipe = pipeline("text-generation", model=model_name, device="cpu")
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  # Define the system prompt
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+ system_prompt = {"role": "system", "content": "You are an AI assistant. Your task is to generate a detailed and comprehensive response."}
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  # Define the Gradio interface
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+ def generate_response(history, user_input, temperature, max_tokens):
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  messages = [system_prompt] + history + [{"role": "user", "content": user_input}]
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+ response = pipe(messages, max_length=max_tokens, temperature=temperature, return_full_text=False)
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  generated_text = response[0]['generated_text']
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  history.append({"role": "user", "content": user_input})
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  history.append({"role": "assistant", "content": generated_text})
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  return history, ""
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+ # Function to clear chat history
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+ def clear_chat():
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+ return [], ""
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+
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  # Create the Gradio interface
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  with gr.Blocks() as demo:
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  gr.Markdown("## Cotype-Nano Text Generation Chat")
 
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  with gr.Row():
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  txt = gr.Textbox(
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  show_label=False,
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+ placeholder="Type your message here...",
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  )
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+ send_btn = gr.Button("Send")
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+
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+ with gr.Row():
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+ clear_btn = gr.Button("Clear Chat")
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+
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+ with gr.Row():
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+ temperature_slider = gr.Slider(0, 1, 0.7, step=0.1, label="Temperature")
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+ max_tokens_slider = gr.Slider(1, 1000, 100, step=1, label="Max Tokens")
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+
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+ send_btn.click(generate_response, [chatbot, txt, temperature_slider, max_tokens_slider], [chatbot, txt])
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+ txt.submit(generate_response, [chatbot, txt, temperature_slider, max_tokens_slider], [chatbot, txt])
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+ clear_btn.click(clear_chat, outputs=[chatbot, txt])
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  # Launch the interface
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  if __name__ == "__main__":