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import gradio as gr
from llm import end_interview, get_problem, send_request
languages_list = ["python", "javascript", "html", "css", "typescript", "dockerfile", "shell", "r", "sql"] # limited by gradio for now
topics_list = ["Arrays", "Strings", "Linked Lists"]
models = ["gpt-3.5-turbo"]
with gr.Blocks() as demo:
gr.Markdown("Your coding interview practice AI assistant!")
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("Start")
# TODO: select LLM model
with gr.Accordion("Solution", open=True) as solution_acc:
description = gr.Markdown()
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=20)
message = gr.Textbox(label="Message", lines=1)
with gr.Column(scale=1):
chat = gr.Chatbot(label="Chat history")
end_btn = gr.Button("Finish the interview")
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,
)
message.submit(
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)
demo.launch()
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