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Commit ·
a1e7e96
1
Parent(s): 8e5545e
Update Hugging Space
Browse files- app.py +38 -38
- requirements.txt +4 -1
app.py
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import gradio as gr
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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("HuggingFaceH4/zephyr-7b-beta")
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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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messages = [{"role": "system", "content": system_message}]
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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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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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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demo = gr.ChatInterface(
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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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],
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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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import os
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from openai import OpenAI
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from dotenv import load_dotenv
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load_dotenv()
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api_key = os.getenv("OPENAI_API_KEY")
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client = OpenAI(api_key=api_key)
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# Initialize an empty history list
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history = []
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def get_response(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
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# Prepare the messages for the API call
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messages = [{"role": "system", "content": system_message}]
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for user_msg, ai_msg in history:
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if user_msg:
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messages.append({"role": "user", "content": user_msg})
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if ai_msg:
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messages.append({"role": "assistant", "content": ai_msg})
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messages.append({"role": "user", "content": message})
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# Call OpenAI API
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response = client.chat.completions.create(
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model="gpt-4-turbo",
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p
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)
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# Extract the response content
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ai_response = response.choices[0].message.content
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# Append the new messages to the history
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history.append((message, ai_response))
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# Format the response for display
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response_text = "\n".join([f"User: {msg[0]}\nAI: {msg[1]}" for msg in history])
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return response_text, history
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# Create the Gradio ChatInterface
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demo = gr.ChatInterface(
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get_response,
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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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],
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)
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if __name__ == "__main__":
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demo.launch()
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requirements.txt
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huggingface_hub==0.22.2
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huggingface_hub==0.22.2
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gradio
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openai
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python-dotenv
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