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Update app.py
Browse files
app.py
CHANGED
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@@ -17,16 +17,30 @@ def respond(
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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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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": message})
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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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@@ -35,27 +49,17 @@ def respond(
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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response += token
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yield response
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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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respond,
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additional_inputs=[
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gr.Textbox(value="
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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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messages = [{"role": "system", "content": system_message}]
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# Add a few-shot context to guide the chatbot
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rc_qa_examples = [
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("What is Resilient Coders?", "Resilient Coders is a nonprofit that trains young people of color for careers in tech."),
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("Is the bootcamp free?", "Yes, the bootcamp is completely free and includes a stipend."),
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("How long is the program?", "It usually runs for about 14 to 20 weeks."),
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("Do I need to know how to code?", "No prior experience is required. We train participants from the ground up."),
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("Is it remote or in-person?", "The program may be remote, in-person, or hybrid depending on the cohort."),
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]
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for q, a in rc_qa_examples:
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messages.append({"role": "user", "content": q})
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messages.append({"role": "assistant", "content": a})
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# Add conversation history
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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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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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# Append current user message
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messages.append({"role": "user", "content": message})
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# Stream response
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response = ""
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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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top_p=top_p,
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):
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token = message.choices[0].delta.content
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response += token
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yield response
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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="You are a helpful assistant that only answers questions about the Resilient Coders bootcamp. If the question is unrelated, politely decline.", 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(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
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],
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)
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