app.py

# ✅ FINAL MERGED & ENHANCED CODE: AI Interview Bot (Jason Voice, One-by-One, Stylish UI)

import os
import gradio as gr
import whisper
import tempfile
import re
from dotenv import load_dotenv
from TTS.api import TTS
import google.generativeai as genai

# -----------------------------
# ✅ CONFIGURE GEMINI API
# -----------------------------
genai.configure(api_key="AIzaSyAeNCjKJVCT0gmQRAPq4NltXkc-1zELH28")
model = genai.GenerativeModel("gemini-1.5-flash-latest")

# -----------------------------
# ✅ LOAD MODELS
# -----------------------------
asr_model = whisper.load_model("base")
jason_tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False, gpu=False)

# -----------------------------
# ✅ GLOBAL STATE
# -----------------------------
candidate_name = ""
field = ""
interview_questions = []
current_question_index = 0
feedback_summary = []

# -----------------------------
# ✅ STEP 1: Collect Candidate Info
# -----------------------------
def save_user_info(name, user_field):
    global candidate_name, field
    candidate_name = name
    field = user_field
    greeting = f"Welcome {name}! Jason will conduct your mock interview for the {field} internship."
    return greeting

# -----------------------------
# ✅ STEP 2: Generate Questions with Strict Format
# -----------------------------
def generate_questions():
    global field, interview_questions, current_question_index, feedback_summary
    current_question_index = 0
    feedback_summary = []

    prompt = (
        f"You are a professional interviewer. Generate exactly 12 internship questions (7 technical + 5 behavioral) for the field of {field}.\n"
        f"Output only the questions, each starting with a number (e.g., 1. ..., 2. ..., ..., 12.). Do not include headings or explanations."
    )
    try:
        response = model.generate_content(prompt)
        raw_output = response.text.strip()
        lines = re.findall(r"^\d+\.\s+.*", raw_output, re.MULTILINE)
        interview_questions = [line.strip() for line in lines]

        if len(interview_questions) != 12:
            return "⚠️ Gemini did not return exactly 12 questions. Please try again.", ""

        return "✅ Questions generated successfully!", interview_questions[0]
    except Exception as e:
        return f"Error: {str(e)}", ""

# -----------------------------
# ✅ STEP 3: Jason Speaks Current Question
# -----------------------------
def speak_current_question():
    if not interview_questions or current_question_index >= len(interview_questions):
        return "No more questions.", None

    question = interview_questions[current_question_index]
    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
    jason_tts.tts_to_file(text=question, file_path=temp_file.name)
    return question, temp_file.name

# -----------------------------
# ✅ STEP 4: Record Answer & Store for Analysis
# -----------------------------
def record_answer(audio_file):
    global current_question_index
    if not interview_questions:
        return "❌ Generate questions first.", ""
    if audio_file is None:
        return "❌ Please record your answer.", ""

    try:
        result = asr_model.transcribe(audio_file)
        transcript = result["text"]
        question = interview_questions[current_question_index]
        feedback_summary.append({"question": question, "answer": transcript, "feedback": None})

        current_question_index += 1
        next_q = interview_questions[current_question_index] if current_question_index < len(interview_questions) else "✅ Interview complete. Click SUBMIT for feedback."
        return f"✅ Answer recorded for Q{current_question_index}.", next_q
    except Exception as e:
        return f"Error: {str(e)}", ""

# -----------------------------
# ✅ STEP 5: Analyze All Answers via Gemini
# -----------------------------
def analyze_all():
    if not feedback_summary:
        return "❌ No answers recorded."

    final_summary = ""
    for i, item in enumerate(feedback_summary, 1):
        prompt = (
            f"Interview Question: {item['question']}\n"
            f"Candidate's Answer: {item['answer']}\n"
            "Evaluate the answer:\n"
            "- Overall Rating: Weak/Average/Good/Excellent\n"
            "- Score: out of 100\n"
            "- Suggestions:"
        )
        try:
            response = model.generate_content(prompt)
            feedback = response.text.strip()
            item['feedback'] = feedback
            final_summary += f"Q{i}: {item['question']}\nAnswer: {item['answer']}\n{feedback}\n{'-'*50}\n"
        except Exception as e:
            final_summary += f"Q{i}: {item['question']}\nAnswer: {item['answer']}\nError: {str(e)}\n{'-'*50}\n"
    return final_summary

# -----------------------------
# ✅ GRADIO UI (Modern Look)
# -----------------------------
with gr.Blocks(theme=gr.themes.Soft(primary_hue="violet")) as demo:
    gr.Markdown("""
    <div style='text-align: center; font-size: 28px; font-weight: bold;'>🎙️ AI Mock Interview Bot</div>
    <div style='text-align: center; font-size: 16px;'>Choose your field and get started. Jason will guide you through your interview.</div>
    """)

    with gr.Row():
        name = gr.Textbox(label="👤 Your Name")
        field = gr.Textbox(label="🎯 Interview Field (e.g. Software, Marketing, HR)")
        start_btn = gr.Button("🚀 Begin Interview")
    greet = gr.Textbox(label="Welcome Message", interactive=False)
    start_btn.click(save_user_info, inputs=[name, field], outputs=greet)

    with gr.Row():
        gen_btn = gr.Button("📄 Generate Questions")
        status = gr.Textbox(label="Status")
        current_q = gr.Textbox(label="📌 Current Question", lines=2)
    gen_btn.click(generate_questions, outputs=[status, current_q])

    speak = gr.Button("🔊 Hear Question", elem_classes="speak-btn")
    audio = gr.Audio(label="🔈 Jason's Voice")
    speak.click(fn=speak_current_question, outputs=[current_q, audio])

    record = gr.Audio(sources=["microphone"], type="filepath", label="🎤 Record Your Answer")
    submit_ans = gr.Button("✅ Submit This Answer")
    rec_status = gr.Textbox(label="Answer Status")
    next_q = gr.Textbox(label="Next Question")
    submit_ans.click(fn=record_answer, inputs=record, outputs=[rec_status, current_q])

    submit_all = gr.Button("📊 Submit All & Get Feedback")
    analysis = gr.Textbox(label="📋 Final Feedback Summary", lines=20)
    submit_all.click(fn=analyze_all, outputs=analysis)

# -----------------------------
# ✅ RUN APP
# -----------------------------
if __name__ == "__main__":
    demo.launch()