import gradio as gr import numpy as np from resources.data import fixed_messages, topic_lists from utils.ui import add_candidate_message, add_interviewer_message def get_problem_solving_ui(llm, tts, stt, default_audio_params, audio_output, name="Coding", interview_type="coding"): with gr.Tab(name, render=False, elem_id=f"{interview_type}_tab") as problem_tab: chat_history = gr.State([]) previous_code = gr.State("") started_coding = gr.State(False) interview_type_var = gr.State(interview_type) with gr.Accordion("Settings") as init_acc: with gr.Row(): with gr.Column(): gr.Markdown("##### Problem settings") with gr.Row(): gr.Markdown("Difficulty") difficulty_select = gr.Dropdown( label="Select difficulty", choices=["Easy", "Medium", "Hard"], value="Medium", container=False, allow_custom_value=True, elem_id=f"{interview_type}_difficulty_select", ) with gr.Row(): topics = topic_lists[interview_type].copy() np.random.shuffle(topics) gr.Markdown("Topic (can type custom value)") topic_select = gr.Dropdown( label="Select topic", choices=topics, value=topics[0], container=False, allow_custom_value=True, elem_id=f"{interview_type}_topic_select", ) with gr.Column(scale=2): requirements = gr.Textbox( label="Requirements", placeholder="Specify additional requirements", lines=5, elem_id=f"{interview_type}_requirements", ) start_btn = gr.Button("Generate a problem", elem_id=f"{interview_type}_start_btn") with gr.Accordion("Problem statement", open=True) as problem_acc: description = gr.Markdown(elem_id=f"{interview_type}_problem_description") with gr.Accordion("Solution", open=False) as solution_acc: with gr.Row() as content: with gr.Column(scale=2): if interview_type == "coding": code = gr.Code( label="Please write your code here. You can use any language, but only Python syntax highlighting is available.", language="python", lines=46, elem_id=f"{interview_type}_code", ) elif interview_type == "sql": code = gr.Code( label="Please write your query here.", language="sql", lines=46, elem_id=f"{interview_type}_code", ) else: code = gr.Code( label="Please write any notes for your solution here.", language=None, lines=46, elem_id=f"{interview_type}_code", ) with gr.Column(scale=1): end_btn = gr.Button("Finish the interview", interactive=False, variant="stop", elem_id=f"{interview_type}_end_btn") chat = gr.Chatbot(label="Chat", show_label=False, show_share_button=False, elem_id=f"{interview_type}_chat") message = gr.Textbox( label="Message", show_label=False, lines=3, max_lines=3, interactive=True, container=False, elem_id=f"{interview_type}_message", ) send_btn = gr.Button("Send", interactive=False, elem_id=f"{interview_type}_send_btn") audio_input = gr.Audio(interactive=False, **default_audio_params, elem_id=f"{interview_type}_audio_input") audio_buffer = gr.State(np.array([], dtype=np.int16)) transcript = gr.State({"words": [], "not_confirmed": 0, "last_cutoff": 0, "text": ""}) with gr.Accordion("Feedback", open=True) as feedback_acc: feedback = gr.Markdown(elem_id=f"{interview_type}_feedback") start_btn.click(fn=add_interviewer_message(fixed_messages["start"]), inputs=[chat], outputs=[chat]).success( fn=lambda: True, outputs=[started_coding] ).success(fn=tts.read_last_message, inputs=[chat], outputs=[audio_output]).success( fn=lambda: (gr.update(open=False), gr.update(interactive=False)), outputs=[init_acc, start_btn] ).success( fn=llm.get_problem, inputs=[requirements, difficulty_select, topic_select, interview_type_var], outputs=[description], scroll_to_output=True, ).success( fn=llm.init_bot, inputs=[description, interview_type_var], outputs=[chat_history] ).success( fn=lambda: (gr.update(open=True), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)), outputs=[solution_acc, end_btn, audio_input, send_btn], ) end_btn.click( fn=add_interviewer_message(fixed_messages["end"]), inputs=[chat], outputs=[chat], ).success(fn=tts.read_last_message, inputs=[chat], outputs=[audio_output]).success( fn=lambda: ( gr.update(open=False), gr.update(interactive=False), gr.update(open=False), gr.update(interactive=False), gr.update(interactive=False), ), outputs=[solution_acc, end_btn, problem_acc, audio_input, send_btn], ).success( fn=llm.end_interview, inputs=[description, chat_history, interview_type_var], outputs=[feedback] ) send_btn.click(fn=add_candidate_message, inputs=[message, chat], outputs=[chat]).success( fn=lambda: None, outputs=[message] ).success( fn=llm.send_request, inputs=[code, previous_code, chat_history, chat], outputs=[chat_history, chat, previous_code], ).success( fn=tts.read_last_message, inputs=[chat], outputs=[audio_output] ).success( fn=lambda: np.array([], dtype=np.int16), outputs=[audio_buffer] ).success( fn=lambda: {"words": [], "not_confirmed": 0, "last_cutoff": 0, "text": ""}, outputs=[transcript] ) if stt.streaming: audio_input.stream( stt.process_audio_chunk, inputs=[audio_input, audio_buffer, transcript], outputs=[transcript, audio_buffer, message], show_progress="hidden", ) audio_input.stop_recording(fn=lambda: gr.update(interactive=True), outputs=[send_btn]) else: audio_input.stop_recording(fn=stt.speech_to_text_full, inputs=[audio_input], outputs=[message]).success( fn=lambda: gr.update(interactive=True), outputs=[send_btn] ).success(fn=lambda: None, outputs=[audio_input]) # TODO: add proper messages and clean up when changing the interview type # problem_tab.select(fn=add_interviewer_message(fixed_messages["intro"]), inputs=[chat, started_coding], outputs=[chat]).success( # fn=tts.read_last_message, inputs=[chat], outputs=[audio_output] # ) return problem_tab