app.py

import gradio as gr
from huggingface_hub import InferenceClient
import re
import random

# Load questions (your original backend)
def load_questions(file_path):
    with open(file_path, 'r') as f:
        data = f.read()
    question_blocks = re.split(r'Question:\s*', data)[1:]
    questions = []
    for block in question_blocks:
        parts = block.split('Possible Answers:')
        question_text = parts[0].strip()
        answers_text = parts[1].strip()
        possible_answers = [ans.strip() for ans in re.split(r'\d+\.\s+', answers_text) if ans.strip()]
        questions.append({'question': question_text, 'answers': possible_answers})
    return questions

all_questions = load_questions('knowledge.txt')

# Question categorization (same as your existing code)
questions_by_type = {
    'Technical': [q for q in all_questions if any(keyword in q['question'].lower() for keyword in [
        'function', 'linked list', 'url', 'rest', 'graphql', 'garbage', 'cap theorem', 'sql', 'hash table',
        'stack', 'queue', 'recursion', 'reverse', 'bfs', 'dfs', 'time complexity', 'binary search tree',
        'web application', 'chat system', 'load balancing', 'caching', 'normalization', 'acid', 'indexing',
        'sql injection', 'https', 'xss', 'hash', 'vulnerabilities'])],
    'Competency-Based Interview': [q for q in all_questions if any(keyword in q['question'].lower() for keyword in [
        "debugging", "learning", "deadlines", "teamwork", "leadership", "mistake", "conflict", "decision"])],
    'Case': [q for q in all_questions if any(keyword in q['question'].lower() for keyword in [
        "testing", "financial", "automation", "analysis", "regression", "business", "stakeholder"])] 
}

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Backend logic (all functions same as before — no changes)
def set_type(choice, user_profile):
    user_profile["interview_type"] = choice
    return "Great! What’s your background and what field/role are you aiming for?", user_profile

def save_background(info, user_profile):
    user_profile["field"] = info
    return "Awesome! Type 'start' below to begin your interview.", user_profile

def respond(message, chat_history, user_profile):
    message_lower = message.strip().lower()

    if not user_profile.get("interview_type") or not user_profile.get("field"):
        bot_msg = "Please finish steps 1 and 2 before starting the interview."
        chat_history.append((message, bot_msg))
        return chat_history

    if message_lower == 'start':
        interview_type = user_profile['interview_type']
        selected_questions = questions_by_type.get(interview_type, [])
        random.shuffle(selected_questions)
        selected_questions = selected_questions[:10]

        user_profile['questions'] = selected_questions
        user_profile['current_q'] = 0
        user_profile['user_answers'] = []
        user_profile['interview_in_progress'] = True

        intro = f"Welcome to your {interview_type} interview for a {user_profile['field']} position. I will ask you up to 10 questions. Type 'stop' anytime to end."
        first_q = f"First question: {selected_questions[0]['question']}"
        chat_history.append((message, intro))
        chat_history.append(("", first_q))
        return chat_history

    if message_lower == 'stop' and user_profile.get("interview_in_progress"):
        user_profile['interview_in_progress'] = False
        bot_msg = "Interview stopped. Type 'feedback' if you'd like me to analyze your answers."
        chat_history.append((message, bot_msg))
        return chat_history

    if user_profile.get("interview_in_progress"):
        q_index = user_profile['current_q']
        user_profile['user_answers'].append(message)
        q_index += 1
        user_profile['current_q'] = q_index

        if q_index < len(user_profile['questions']):
            bot_msg = f"Next question: {user_profile['questions'][q_index]['question']}"
        else:
            user_profile['interview_in_progress'] = False
            bot_msg = "Interview complete! Type 'feedback' if you'd like me to analyze your answers."
        chat_history.append((message, bot_msg))
        return chat_history

    if message_lower == 'feedback':
        feedback = generate_feedback(user_profile)
        chat_history.append((message, feedback))
        return chat_history

    # Normal chatbot conversation
    messages = [{"role": "system", "content": f"You are a professional interviewer conducting a {user_profile['interview_type']} interview for a candidate in {user_profile['field']}."}]
    for q, a in chat_history:
        messages.append({"role": "user", "content": q})
        messages.append({"role": "assistant", "content": a})
    messages.append({"role": "user", "content": message})

    response = client.chat_completion(messages, max_tokens=150, stream=False)
    bot_msg = response.choices[0].message.content
    chat_history.append((message, bot_msg))
    return chat_history

def generate_feedback(user_profile):
    feedback = []
    questions = user_profile.get('questions', [])
    answers = user_profile.get('user_answers', [])
    for i, user_ans in enumerate(answers):
        correct_answers = questions[i]['answers']
        match = any(ans.lower() in user_ans.lower() for ans in correct_answers)
        if match:
            fb = f"Question {i+1}: ✅ Good job!"
        else:
            fb = f"Question {i+1}: ❌ Missed key points: {correct_answers[0]}"
        feedback.append(fb)
    return "\n".join(feedback)


# The new Intervu 2.0 UI with your design!
with gr.Blocks(css="""
    body { background-color: #f2f4f8; font-family: 'Segoe UI', sans-serif; }
    h1 { text-align: center; color: #2c3e50; }
    img { display: block; margin: auto; width: 100px; border-radius: 20px; }
    button { font-size: 16px; padding: 10px 20px; border-radius: 10px; border: none; background-color: #3B82F6; color: white; }
    button:hover { background-color: #2563EB; }
    .gr-chatbot { background-color: white; border-radius: 15px; padding: 20px; }
""") as demo:

   
    gr.Markdown("![image/png](https://cdn-uploads.huggingface.co/production/uploads/6841b10b397a67a7c7a39b89/eG45tcJZ-GsK4wVnWiC55.png)")
    
    user_profile = gr.State({"interview_type": "", "field": "", "interview_in_progress": False})
    chat_history = gr.State([])

    # Header
    gr.Markdown("""
    <div style='text-align:center;'>
        <img src="Untitled design.jpg">
        <h1>Welcome to <b>Intervu</b></h1>
    </div>
    """)

    # Step 1 - Choose Interview Type
    gr.Markdown("### Step 1: Choose Interview Type")
    with gr.Row():
        btn1 = gr.Button("Technical")
        btn2 = gr.Button("Competency-Based Interview")
        btn3 = gr.Button("Case")
    type_output = gr.Textbox(label="Bot response", interactive=False)

    btn1.click(set_type, inputs=[gr.Textbox(value="Technical", visible=False), user_profile], outputs=[type_output, user_profile])
    btn2.click(set_type, inputs=[gr.Textbox(value="Competency-Based Interview", visible=False), user_profile], outputs=[type_output, user_profile])
    btn3.click(set_type, inputs=[gr.Textbox(value="Case", visible=False), user_profile], outputs=[type_output, user_profile])

    # Step 2 - Enter Background
    gr.Markdown("### Step 2: Enter Your Background")
    background = gr.Textbox(label="Your background and field/goal")
    background_btn = gr.Button("Submit")
    background_output = gr.Textbox(label="Bot response", interactive=False)
    background_btn.click(save_background, inputs=[background, user_profile], outputs=[background_output, user_profile])

    # Step 3 - Chatbot Mode Selection
    gr.Markdown("### Choose Chat Mode")
    with gr.Row():
        gr.Button("Text-Based")  # You can build voice & webcam later :)

    # Chat interface
    chatbot = gr.Chatbot(label="Interview Chat")
    msg = gr.Textbox(label="Type 'start' to begin")
    send_btn = gr.Button("Send")
    send_btn.click(respond, inputs=[msg, chat_history, user_profile], outputs=[chatbot], queue=False)
    send_btn.click(lambda: "", None, msg, queue=False)

demo.launch()