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import os |
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import logging |
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import gradio as gr |
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from openai import OpenAI |
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from dotenv import load_dotenv |
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load_dotenv(".env") |
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logging.basicConfig(level=logging.INFO) |
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logging.getLogger("gradio").setLevel(logging.INFO) |
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logging.getLogger("httpx").setLevel(logging.WARNING) |
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client = OpenAI() |
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def generate_completion(input, history): |
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messages = [ |
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{ |
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"role": "system", |
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"content": "You are a world-class assistant.", |
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} |
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] |
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if history: |
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for entry in history: |
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if len(entry) == 2: |
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messages.append( |
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{ |
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"role": "user", |
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"content": entry[0], |
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} |
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) |
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messages.append( |
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{ |
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"role": "assistant", |
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"content": entry[1], |
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} |
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) |
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messages.append( |
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{ |
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"role": "user", |
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"content": input, |
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} |
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) |
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response = client.chat.completions.create( |
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model="gpt-3.5-turbo-0125", |
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messages=messages, |
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stream=True, |
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temperature=0, |
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max_tokens=4000, |
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) |
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answer_str: str = "" |
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for chunk in response: |
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if chunk.choices[0].delta.content is not None: |
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answer_str += chunk.choices[0].delta.content |
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else: |
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answer_str += "" |
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yield answer_str |
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demo = gr.ChatInterface(fn=generate_completion) |
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if __name__ == "__main__": |
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demo.queue() |
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demo.launch() |
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