mianoemm commited on
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mindmatterskwk

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Files changed (1) hide show
  1. app.py +17 -62
app.py CHANGED
@@ -1,69 +1,24 @@
1
  import gradio as gr
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  from huggingface_hub import InferenceClient
3
 
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-
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- def respond(
6
- message,
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- history: list[dict[str, str]],
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- system_message,
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- max_tokens,
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- temperature,
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- top_p,
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- hf_token: gr.OAuthToken,
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- ):
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- """
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- For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
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- """
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- client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
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-
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- messages = [{"role": "system", "content": system_message}]
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-
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- messages.extend(history)
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-
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  messages.append({"role": "user", "content": message})
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-
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- response = ""
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-
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- for message in client.chat_completion(
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  messages,
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- max_tokens=max_tokens,
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- stream=True,
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- temperature=temperature,
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- top_p=top_p,
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- ):
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- choices = message.choices
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- token = ""
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- if len(choices) and choices[0].delta.content:
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- token = choices[0].delta.content
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-
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- response += token
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- yield response
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-
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-
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- """
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- For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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- """
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- chatbot = gr.ChatInterface(
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- respond,
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- additional_inputs=[
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- gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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- gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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- gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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- gr.Slider(
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- minimum=0.1,
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- maximum=1.0,
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- value=0.95,
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- step=0.05,
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- label="Top-p (nucleus sampling)",
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- ),
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- ],
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- )
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-
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- with gr.Blocks() as demo:
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- with gr.Sidebar():
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- gr.LoginButton()
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- chatbot.render()
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- if __name__ == "__main__":
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- demo.launch()
 
1
  import gradio as gr
2
  from huggingface_hub import InferenceClient
3
 
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+ client = InferenceClient("Qwen/Qwen2.5-7B-Instruct")
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+
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+ def respond(message, history):
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+
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+ messages = [{"role": "system", "content": "You are a friendly chatbot."}]
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+
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+ if history:
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+ messages.extend(history)
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+
 
 
 
 
 
 
 
 
 
 
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  messages.append({"role": "user", "content": message})
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+
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+ response = client.chat_completion(
 
 
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  messages,
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+ max_tokens=100
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+ )
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+
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+ return response.choices[0].message.content.strip()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ chatbot = gr.ChatInterface(respond)
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+ chatbot.launch()