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| import gradio as gr | |
| import openai | |
| # ========================= | |
| # 1) OpenAI API Key | |
| # ========================= | |
| openai.api_key = "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" | |
| # ========================= | |
| # 2) Recruiter first lines | |
| # ========================= | |
| FIRST_LINES = { | |
| "Friendly": "I see you took a few years off. That’s interesting — tell me more about what you learned during that time.", | |
| "Tough": "A gap that long raises questions. Why should we consider you over someone with recent experience?" | |
| } | |
| # ========================= | |
| # 3) GPT reply for recruiter | |
| # ========================= | |
| def get_recruiter_reply(user_text, style, history): | |
| prompt = f""" | |
| You are a recruiter in an interview. Tone = {style} (friendly or tough). | |
| Conversation so far: | |
| {history} | |
| Candidate just said: {user_text} | |
| Rules: | |
| - Keep replies under 25 words | |
| - Ask one probing question at a time | |
| - Avoid repeating previous questions | |
| Context: mid-career woman re-entering the workforce | |
| Return the next recruiter question only. | |
| """ | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4o-mini", | |
| messages=[{"role":"user", "content":prompt}] | |
| ) | |
| return response.choices[0].message.content.strip() | |
| # ========================= | |
| # 4) GPT feedback after 10 exchanges | |
| # ========================= | |
| def get_feedback(history): | |
| prompt = f""" | |
| Based on this 10-turn transcript: | |
| {history} | |
| Return concise feedback: | |
| - Confidence (0-10) | |
| - Strengths (2 bullets) | |
| - One improvement tip | |
| - Example reframe sentence (<=20 words) | |
| """ | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4o-mini", | |
| messages=[{"role":"user", "content":prompt}] | |
| ) | |
| return response.choices[0].message.content.strip() | |
| # ========================= | |
| # 5) Dummy TTS function (Gradio handles text-to-speech) | |
| # ========================= | |
| def speak(text): | |
| """ | |
| No external TTS API needed. | |
| Gradio will synthesize audio automatically. | |
| """ | |
| return text | |
| # ========================= | |
| # 6) Conversation handler | |
| # ========================= | |
| def run_boosty(style, user_voice, history="", turn=1): | |
| # Step 1: First recruiter line | |
| if turn == 1: | |
| first_line = FIRST_LINES[style] | |
| history += f"Recruiter ({style}): {first_line}\n" | |
| audio = speak(first_line) | |
| return "", history, audio, turn + 1, "" | |
| # Steps 2-10: transcribe user voice using OpenAI Whisper API | |
| with open(user_voice.name, "rb") as f: | |
| transcription = openai.Audio.transcriptions.create(file=f, model="whisper-1")["text"] | |
| history += f"User: {transcription}\n" | |
| # After 10 exchanges → feedback | |
| if turn > 10: | |
| feedback = get_feedback(history) | |
| return "", history, None, turn, feedback | |
| # Generate next recruiter line | |
| reply = get_recruiter_reply(transcription, style, history) | |
| history += f"Recruiter: {reply}\n" | |
| audio = speak(reply) | |
| return "", history, audio, turn + 1, "" | |
| # ========================= | |
| # 7) Gradio interface | |
| # ========================= | |
| with gr.Blocks() as demo: | |
| style_var = gr.Radio(["Friendly", "Tough"], label="Choose recruiter style") | |
| user_voice = gr.Audio(source="microphone", type="file", label="Speak your reply") | |
| history = gr.Textbox(label="Transcript", interactive=False) | |
| audio_output = gr.Audio(label="Recruiter voice", type="auto", interactive=False) | |
| turn_var = gr.State(value=1) | |
| feedback_text = gr.Textbox(label="Feedback", interactive=False) | |
| submit_btn = gr.Button("Next") | |
| submit_btn.click( | |
| fn=run_boosty, | |
| inputs=[style_var, user_voice, history, turn_var], | |
| outputs=[style_var, history, audio_output, turn_var, feedback_text] | |
| ) | |
| demo.launch() | |