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import gradio as gr |
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from openai import OpenAI |
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import os |
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ACCESS_TOKEN = os.getenv("HF_TOKEN") |
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print("Access token loaded.") |
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client = OpenAI( |
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base_url="https://api-inference.huggingface.co/v1/", |
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api_key=ACCESS_TOKEN, |
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) |
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print("OpenAI client initialized.") |
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def respond( |
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message, |
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history: list[tuple[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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): |
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print(f"Received message: {message}") |
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print(f"History: {history}") |
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print(f"System message: {system_message}") |
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print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}") |
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messages = [{"role": "system", "content": system_message}] |
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for val in history: |
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if val[0]: |
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messages.append({"role": "user", "content": val[0]}) |
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print(f"Added user message to context: {val[0]}") |
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if val[1]: |
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messages.append({"role": "assistant", "content": val[1]}) |
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print(f"Added assistant message to context: {val[1]}") |
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messages.append({"role": "user", "content": message}) |
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response = "" |
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print("Sending request to OpenAI API.") |
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for message in client.chat.completions.create( |
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model="PowerInfer/SmallThinker-3B-Preview", |
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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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messages=messages, |
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): |
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token = message.choices[0].delta.content |
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print(f"Received token: {token}") |
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response += token |
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yield response |
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print("Completed response generation.") |
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chatbot = gr.Chatbot(height=600) |
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print("Chatbot interface created.") |
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demo = gr.ChatInterface( |
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respond, |
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additional_inputs=[ |
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gr.Textbox(value="", label="System message"), |
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gr.Slider(minimum=1, maximum=4096, 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", |
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), |
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], |
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fill_height=True, |
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chatbot=chatbot, |
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theme="Nymbo/Nymbo_Theme", |
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) |
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print("Gradio interface initialized.") |
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if __name__ == "__main__": |
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print("Launching the demo application.") |
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demo.launch() |