import os import gradio as gr import numpy as np from api.audio import STTManager, TTSManager from api.llm import LLMManager from config import config from docs.instruction import instruction from resources.data import fixed_messages, topics_list from resources.prompts import prompts from utils.ui import add_interviewer_message llm = LLMManager(config, prompts) tts = TTSManager(config) stt = STTManager(config) default_audio_params = { "label": "Record answer", "sources": ["microphone"], "type": "numpy", "waveform_options": {"show_controls": False}, "editable": False, "container": False, "show_share_button": False, "streaming": stt.streaming, } def hide_settings(): init_acc = gr.Accordion("Settings", open=False) start_btn = gr.Button("Generate a problem", interactive=False) return init_acc, start_btn def show_solution(): solution_acc = gr.Accordion("Solution", open=True) end_btn = gr.Button("Finish the interview", interactive=True) audio_input = gr.Audio(interactive=True, **default_audio_params) return solution_acc, end_btn, audio_input 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) audio_input = gr.Audio(interactive=False, **default_audio_params) return solution_acc, end_btn, problem_acc, audio_input def get_status_color(obj): if obj.status: if obj.streaming: return "🟢" return "🟡" return "🔴" # Interface with gr.Blocks(title="AI Interviewer") as demo: if os.getenv("IS_DEMO"): gr.Markdown(instruction["demo"]) started_coding = gr.State(False) audio_output = gr.Audio(label="Play audio", autoplay=True, visible=os.environ.get("DEBUG", False), streaming=tts.streaming) with gr.Tab("Instruction") as instruction_tab: with gr.Row(): with gr.Column(scale=2): gr.Markdown(instruction["introduction"]) with gr.Column(scale=1): space = " " * 10 tts_status = get_status_color(tts) gr.Markdown(f"TTS status: {tts_status}{space}{config.tts.name}") stt_status = get_status_color(stt) gr.Markdown(f"STT status: {stt_status}{space}{config.stt.name}") llm_status = get_status_color(llm) gr.Markdown(f"LLM status: {llm_status}{space}{config.llm.name}") gr.Markdown(instruction["quick_start"]) with gr.Row(): with gr.Column(scale=2): gr.Markdown(instruction["interface"]) with gr.Column(scale=1): gr.Markdown("Bot interaction area will look like this. Use Record button to record your answer.") chat_example = gr.Chatbot( label="Chat", show_label=False, show_share_button=False, value=[["Candidate message", "Interviewer message"]] ) audio_input_example = gr.Audio(interactive=True, **default_audio_params) gr.Markdown(instruction["models"]) gr.Markdown(instruction["acknowledgements"]) gr.Markdown(instruction["legal"]) with gr.Tab("Coding") as coding_tab: chat_history = gr.State([]) previous_code = gr.State("") 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, ) with gr.Row(): gr.Markdown("Topic (can type custom value)") topic_select = gr.Dropdown( label="Select topic", choices=topics_list, value="Arrays", container=False, allow_custom_value=True ) with gr.Column(scale=2): requirements = gr.Textbox(label="Requirements", placeholder="Specify additional requirements", lines=5) start_btn = gr.Button("Generate a problem") 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): 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, ) with gr.Column(scale=1): end_btn = gr.Button("Finish the interview", interactive=False) chat = gr.Chatbot(label="Chat", show_label=False, show_share_button=False) message = gr.Textbox( label="Message", placeholder="Your message will appear here", show_label=False, lines=3, max_lines=3, interactive=False, ) send_btn = gr.Button("Send", interactive=False) audio_input = gr.Audio(interactive=False, **default_audio_params) 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() # Events coding_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] ) 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=hide_settings, outputs=[init_acc, start_btn] ).success( fn=llm.get_problem, inputs=[requirements, difficulty_select, topic_select], outputs=[description], scroll_to_output=True, ).success( fn=llm.init_bot, inputs=[description], outputs=[chat_history] ).success( fn=show_solution, outputs=[solution_acc, end_btn, audio_input] ) 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=hide_solution, outputs=[solution_acc, end_btn, problem_acc, audio_input]).success( fn=llm.end_interview, inputs=[description, chat_history], outputs=[feedback] ) send_btn.click(fn=stt.add_user_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: gr.Button("Send", interactive=False), outputs=[send_btn] ).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.Button("Send", 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.Button("Send", interactive=True), outputs=[send_btn] ).success(fn=lambda: None, outputs=[audio_input]) demo.launch(show_api=False)