interviewer / app.py
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import gradio as gr
from llm import end_interview, get_problem, read_last_message, send_request, transcribe_audio
from options import languages_list, models, topics_list
def hide_settings():
init_acc = gr.Accordion("Settings", open=False)
start_btn = gr.Button("Generate a problem", interactive=False)
solution_acc = gr.Accordion("Solution", open=True)
end_btn = gr.Button("Finish the interview", interactive=True)
send_btn = gr.Button("Send", interactive=True)
audio_input = gr.Audio(
label="Record audio",
sources=["microphone"],
type="numpy",
waveform_options={"show_controls": False},
interactive=True,
editable=False,
)
chat = [
(
None,
"Welcome to the interview! Please take a moment to read the problem statement. Then you can share you initial thoughts and ask any questions you may have. Good luck!",
)
]
return init_acc, start_btn, solution_acc, end_btn, send_btn, audio_input, chat
def hide_solution():
solution_acc = gr.Accordion("Solution", open=False)
end_btn = gr.Button("Finish the interview", interactive=False)
problem_acc = gr.Accordion("Problem statement", open=False)
send_btn = gr.Button("Send", interactive=False)
audio_input = gr.Audio(
label="Record audio",
sources=["microphone"],
type="numpy",
waveform_options={"show_controls": False},
interactive=False,
editable=False,
)
return solution_acc, end_btn, problem_acc, send_btn, audio_input
def return_none():
return None
with gr.Blocks() as demo:
gr.Markdown("Your coding interview practice AI assistant!")
# TODO: add instructions tab
# TODO: add other types of interviews (e.g. system design, ML design, behavioral, etc.)
with gr.Tab("Coding"):
chat_history = gr.State([])
previous_code = gr.State("")
client = gr.State(None)
with gr.Accordion("Settings") as init_acc:
with gr.Row():
with gr.Column():
gr.Markdown("Difficulty")
difficulty_select = gr.Dropdown(
label="Select difficulty", choices=["Easy", "Medium", "Hard"], value="Medium", container=False
)
gr.Markdown("Topic")
topic_select = gr.Dropdown(
label="Select topic", choices=topics_list, value="Arrays", container=False, allow_custom_value=True
)
gr.Markdown("Select LLM model to use")
model_select = gr.Dropdown(label="Select model", choices=models, value="gpt-3.5-turbo", container=False)
with gr.Column():
requirements = gr.Textbox(
label="Requirements", placeholder="Specify requirements: topic, difficulty, language, etc.", lines=5
)
start_btn = gr.Button("Generate a problem")
# TODO: select LLM model
with gr.Accordion("Problem statement", open=True) as problem_acc:
description = gr.Markdown()
with gr.Accordion("Solution", open=False) as solution_acc:
with gr.Row() as content:
with gr.Column(scale=2):
language_select = gr.Dropdown(
label="Select language", choices=languages_list, value="python", container=False, interactive=True
)
code = gr.Code(label="Solution", language=language_select.value, lines=35)
with gr.Column(scale=1):
end_btn = gr.Button("Finish the interview", interactive=False)
chat = gr.Chatbot(label="Chat history")
audio_input = gr.Audio(
label="Record audio",
sources=["microphone"],
type="numpy",
waveform_options={"show_controls": False},
interactive=False,
editable=False,
)
audio_output = gr.Audio(label="Play audio", autoplay=True, visible=False)
message = gr.Textbox(label="Message", lines=3)
send_btn = gr.Button("Send", interactive=False)
with gr.Accordion("Feedback", open=True) as feedback_acc:
feedback = gr.Markdown()
start_btn.click(
fn=get_problem,
inputs=[requirements, difficulty_select, topic_select, model_select],
outputs=[description, chat_history],
scroll_to_output=True,
).then(fn=hide_settings, inputs=None, outputs=[init_acc, start_btn, solution_acc, end_btn, send_btn, audio_input, chat])
send_btn.click(
fn=send_request,
inputs=[code, previous_code, message, chat_history, chat, model_select],
outputs=[chat_history, chat, message, previous_code],
)
end_btn.click(fn=end_interview, inputs=[chat_history, model_select], outputs=feedback).then(
fn=hide_solution, inputs=None, outputs=[solution_acc, end_btn, problem_acc, send_btn, audio_input]
)
audio_input.stop_recording(fn=transcribe_audio, inputs=[audio_input], outputs=[message]).then(
fn=return_none, inputs=None, outputs=[audio_input]
)
# .then(
# fn=send_request,
# inputs=[code, previous_code, message, chat_history, chat, model_select],
# outputs=[chat_history, chat, message, previous_code],
# )
chat.change(fn=read_last_message, inputs=[chat], outputs=[audio_output])
audio_output.stop(fn=return_none, inputs=None, outputs=[audio_output])
demo.launch()