2024-02-25 21:09:13 -05:00
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import gradio as gr
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from openai import OpenAI
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2024-07-09 12:20:17 -04:00
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client = OpenAI(base_url="http://localhost:8000/v1", api_key="llama.cpp")
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2024-02-25 21:09:13 -05:00
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model = "gpt-3.5-turbo"
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2024-07-09 12:20:17 -04:00
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2024-02-25 21:09:13 -05:00
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def predict(message, history):
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messages = []
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for user_message, assistant_message in history:
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messages.append({"role": "user", "content": user_message})
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messages.append({"role": "assistant", "content": assistant_message})
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2024-07-09 12:20:17 -04:00
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2024-02-25 21:09:13 -05:00
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messages.append({"role": "user", "content": message})
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response = client.chat.completions.create(
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2024-07-09 12:20:17 -04:00
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model=model, messages=messages, stream=True
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2024-02-25 21:09:13 -05:00
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)
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text = ""
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for chunk in response:
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content = chunk.choices[0].delta.content
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if content:
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text += content
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yield text
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js = """function () {
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gradioURL = window.location.href
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if (!gradioURL.endsWith('?__theme=dark')) {
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window.location.replace(gradioURL + '?__theme=dark');
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}
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}"""
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css = """
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footer {
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visibility: hidden;
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}
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full-height {
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height: 100%;
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}
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"""
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with gr.Blocks(theme=gr.themes.Soft(), js=js, css=css, fill_height=True) as demo:
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2024-07-09 12:20:17 -04:00
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gr.ChatInterface(
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predict,
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fill_height=True,
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examples=[
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"What is the capital of France?",
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"Who was the first person on the moon?",
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],
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)
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2024-02-25 21:09:13 -05:00
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if __name__ == "__main__":
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demo.launch()
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