import streamlit as st
import openai
from langchain_google_genai import ChatGoogleGenerativeAI
from datetime import datetime, timedelta
import time

# API keys
GOOGLE_API_KEY = st.secrets["GOOGLE_API_KEY"]
OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]

# Initialize OpenAI
openai.api_key = OPENAI_API_KEY

# In-memory storage for progress tracking
progress_data = {
    "questions_solved": {
        "Behavioral": 0, "Technical": 0, "Situational": 0, "Case Study": 0, "Problem Solving": 0
    },
    "mock_interviews_taken": 0,
    "feedback_provided": 0,
    "tips_retrieved": 0
}

def get_llm(model_choice):
    if model_choice == "Gemini" or model_choice == "Groq":
        return ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=GOOGLE_API_KEY)
    elif model_choice == "OpenAI":
        return None
    else:
        raise ValueError("Unsupported model choice.")

def generate_questions(model_choice, role, question_type, num_questions, difficulty):
    prompt = (
        f"Generate {num_questions} {difficulty} {question_type.lower()} interview questions for the role of {role}. "
        f"Only include {question_type.lower()} questions."
    )
    if model_choice == "OpenAI":
        response = openai.Completion.create(
            engine="text-davinci-003",
            prompt=prompt,
            max_tokens=150
        )
        return response.choices[0].text.strip().split("\n")
    elif model_choice == "Gemini" or model_choice == "Groq":
        llm = get_llm(model_choice)
        response = llm.invoke(prompt)
        return response.content.split("\n")
    else:
        raise ValueError("Unsupported model choice.")

def provide_feedback(model_choice, answer):
    prompt = f"Provide constructive feedback on the following interview answer: {answer}"
    if model_choice == "OpenAI":
        response = openai.Completion.create(
            engine="text-davinci-003",
            prompt=prompt,
            max_tokens=150
        )
        return response.choices[0].text.strip()
    elif model_choice == "Gemini" or model_choice == "Groq":
        llm = get_llm(model_choice)
        response = llm.invoke(prompt)
        return response.content
    else:
        raise ValueError("Unsupported model choice.")

def get_tips(model_choice, role):
    prompt = f"Provide useful interview tips for the role of {role}. Include body language, dress code, etiquette, and role-specific advice."
    if model_choice == "OpenAI":
        response = openai.Completion.create(
            engine="text-davinci-003",
            prompt=prompt,
            max_tokens=150
        )
        return response.choices[0].text.strip()
    elif model_choice == "Gemini" or model_choice == "Groq":
        llm = get_llm(model_choice)
        response = llm.invoke(prompt)
        return response.content
    else:
        raise ValueError("Unsupported model choice.")

def start_mock_interview():
    st.write("### Mock Interview Starting")
    st.write("The mock interview is starting now. Please connect with your interviewer.")
    
    # Simulate video call window with recording message
    st.markdown("""
        <div style="
            width: 100%;
            height: 400px;
            background-color: #000;
            color: #fff;
            display: flex;
            flex-direction: column;
            align-items: center;
            justify-content: center;
            font-size: 24px;
            font-weight: bold;
            border-radius: 8px;
            border: 2px solid #2196F3;
        ">
            <p style="margin: 10px;">
                <i class="fa fa-circle" aria-hidden="true" style="font-size: 50px; color: red;"></i><br>
                Call is being RECORDED
            </p>
            <p style="font-size: 18px;">Interview with: John Doe</p>
        </div>
    """, unsafe_allow_html=True)
    
    countdown_end = datetime.now() + timedelta(seconds=60)  # 60 seconds timer
    
    while datetime.now() < countdown_end:
        remaining_time = countdown_end - datetime.now()
        if remaining_time <= timedelta(seconds=3):
            st.write(f"**Time Remaining:** {str(remaining_time).split('.')[0]}")
        time.sleep(1)
    
    st.write("Mock Interview Session Ended.")
    progress_data["mock_interviews_taken"] += 1

def schedule_mock_interview():
    st.subheader("Schedule a Mock Interview")
    date = st.date_input("Select Date", min_value=datetime.today())
    time = st.time_input("Select Time", value=datetime.now().time())
    duration = st.selectbox("Duration (Minutes)", [30, 45, 60])
    now = datetime.now()
    selected_datetime = datetime.combine(date, time)

    col1, col2 = st.columns(2)
    with col1:
        if st.button("Start Interview Now"):
            start_mock_interview()

    with col2:
        if st.button("Schedule Interview"):
            if selected_datetime < now:
                st.error("Selected time is in the past. Please choose a future time.")
            else:
                # Simulate saving interview schedule
                st.success(f"Mock interview scheduled for {selected_datetime.strftime('%Y-%m-%d %H:%M:%S')} with a duration of {duration} minutes.")
                progress_data["mock_interviews_taken"] += 1

def track_progress():
    st.subheader("Track Your Progress")
    st.write("Here's your detailed progress data:")
    st.markdown(f"""
    <div class="progress-container">
        <p><strong>Behavioral Questions Solved:</strong> {progress_data['questions_solved']['Behavioral']}</p>
        <p><strong>Technical Questions Solved:</strong> {progress_data['questions_solved']['Technical']}</p>
        <p><strong>Situational Questions Solved:</strong> {progress_data['questions_solved']['Situational']}</p>
        <p><strong>Case Study Questions Solved:</strong> {progress_data['questions_solved']['Case Study']}</p>
        <p><strong>Problem Solving Questions Solved:</strong> {progress_data['questions_solved']['Problem Solving']}</p>
        <p><strong>Mock Interviews Taken:</strong> {progress_data['mock_interviews_taken']}</p>
        <p><strong>Feedback Provided:</strong> {progress_data['feedback_provided']}</p>
        <p><strong>Tips Retrieved:</strong> {progress_data['tips_retrieved']}</p>
    </div>
    """, unsafe_allow_html=True)

def connect_resources():
    st.subheader("Connect with Resources")
    st.write("### Articles & Books")
    st.write("1. [The Complete Guide to Job Interviews](https://example.com)")
    st.write("2. [Cracking the Coding Interview](https://example.com)")

    st.write("### Videos")
    st.write("1. [Top 10 Interview Tips](https://example.com)")
    st.write("2. [Behavioral Interview Questions Explained](https://example.com)")

    st.write("### Connect with Career Coaches")
    st.write("If you need personalized help, please fill out the form below or contact us through [Career Coaches Contact](https://example.com).")

    # Form to connect with career coaches or mentors
    with st.form("contact_form"):
        st.write("For personalized assistance, please fill out this form:")
        name = st.text_input("Name")
        email = st.text_input("Email")
        message = st.text_area("Message")
        submit_button = st.form_submit_button("Submit")

        if submit_button:
            if not name or not email or not message:
                st.error("Please fill out all fields.")
            else:
                st.success("Thank you for contacting us! We will get back to you soon.")

def style_output(text, color):
    return f'<div class="output-container"><span style="color: {color}; font-weight: bold;">{text}</span></div>'

# Streamlit app layout
st.set_page_config(page_title="TechPrep", layout="wide")
st.markdown(
    """
    <style>
    body {
        background-color: #e0f7fa; /* Light cyan background color */
        font-family: Arial, sans-serif;
    }
    .stButton>button {
        width: 100%;
        height: 3em;
        font-size: 1.2em;
        color: white;
        background-color: #4CAF50;
        border: none;
        border-radius: 8px;
        cursor: pointer;
        transition: background-color 0.3s ease;
    }
    .stButton>button:hover {
        background-color: #45a049;
    }
    .output-container {
        border: 2px solid #2196F3;
        border-radius: 8px;
        padding: 15px;
        margin: 15px 0;
        background-color: #f9f9f9;
    }
    .sidebar {
        background-color: #ffffff; /* Sidebar background color */
        padding: 1em;
    }
    .footer {
        background-color: #4CAF50;
        color: white;
        text-align: center;
        padding: 1em;
        position: fixed;
        bottom: 0;
        width: 100%;
        border-top: 2px solid #ffffff;
    }
    </style>
    """,
    unsafe_allow_html=True
)

# Show welcome message with an icon for 3-4 seconds
welcome_message = st.empty()
with welcome_message.container():
    st.markdown("""
        <div style="
            text-align: center;
            padding: 20px;
            background-color: #4CAF50;
            color: white;
            border-radius: 8px;
            font-size: 24px;
        ">
            <i class="fa fa-smile-o" aria-hidden="true" style="font-size: 40px;"></i> Welcome to TechPrep!
        </div>
    """, unsafe_allow_html=True)
time.sleep(4)  # Wait for 4 seconds
welcome_message.empty()  # Remove the welcome message

# Initialize session state for questions, answers, and current index
if 'questions' not in st.session_state:
    st.session_state.questions = []
if 'answers' not in st.session_state:
    st.session_state.answers = []
if 'feedback' not in st.session_state:
    st.session_state.feedback = []
if 'question_index' not in st.session_state:
    st.session_state.question_index = 0
if 'show_results' not in st.session_state:
    st.session_state.show_results = False

# Sidebar Navigation
st.sidebar.title("TechPrep Navigation")
nav_option = st.sidebar.radio("Choose an option:", 
    ["Generate Questions", "Mock Interview", "Track Progress", "Connect with Resources"])

# Handling page navigation
if nav_option == "Generate Questions":
    st.header("📝 Generate Interview Questions")
    
    model_choice = st.selectbox("Choose Model:", ["OpenAI", "Gemini", "Groq"])
    role = st.selectbox("Role:", ["GenAI", "ML", "DevOps", "Software Engineer", "Data Scientist", "Product Manager", "Designer", "Business Analyst"])
    question_type = st.selectbox("Question Type:", ["Behavioral", "Technical", "Situational", "Case Study", "Problem Solving"])
    num_questions = st.number_input("Number of Questions:", min_value=1, max_value=20, value=5)
    difficulty = st.selectbox("Difficulty Level:", ["Basic", "Medium", "Complex"])
    
    if st.button("Generate Questions", key="generate_questions"):
        with st.spinner("Generating questions..."):
            questions = generate_questions(model_choice, role, question_type, num_questions, difficulty)
            st.session_state.questions = questions
            st.session_state.answers = ["" for _ in questions]
            st.session_state.feedback = ["" for _ in questions]
            st.session_state.question_index = 0
            st.session_state.show_results = False
            progress_data["questions_solved"][question_type] += num_questions

    # Display questions with navigation
    if st.session_state.questions:
        question_list = st.session_state.questions
        index = st.session_state.question_index
        
        if index < len(question_list):
            st.write(f"**Question {index + 1}:** {question_list[index]}")
            
            # Answer input box
            answer = st.text_area("Your Answer", key="text_area_answer")

            col1, col2 = st.columns(2)
            with col1:
                if index > 0:
                    if st.button("Previous"):
                        st.session_state.question_index -= 1
            
            with col2:
                if index < len(question_list) - 1:
                    if st.button("Next"):
                        st.session_state.question_index += 1

            # Submit answer and provide feedback
            if st.button("Submit Answer"):
                if not answer:
                    st.error("Please enter an answer to receive feedback.")
                else:
                    with st.spinner("Providing feedback..."):
                        feedback = provide_feedback(model_choice, answer)
                        st.session_state.answers[index] = answer
                        st.session_state.feedback[index] = feedback
                        st.markdown(style_output("Feedback Received:", "#FF5722"), unsafe_allow_html=True)
                        st.write(feedback)
                        progress_data["feedback_provided"] += 1

            # Show results and score when all questions have been answered
            if index == len(question_list) - 1:
                st.session_state.show_results = True

        if st.session_state.show_results:
            st.write("### Your Results")
            total_questions = len(st.session_state.questions)
            answered_questions = sum(1 for ans in st.session_state.answers if ans)
            score = (answered_questions / total_questions) * 100
            st.write(f"**Score:** {score:.2f}%")
            
            # Display feedback and tips
            st.write("### Feedback Summary")
            for i, (q, ans, fb) in enumerate(zip(st.session_state.questions, st.session_state.answers, st.session_state.feedback)):
                st.write(f"**Question {i + 1}:** {q}")
                st.write(f"**Your Answer:** {ans}")
                st.write(f"**Feedback:** {fb}")

            tips = get_tips(model_choice, role)
            st.write("### Tips to Improve")
            st.write(tips)
            progress_data["tips_retrieved"] += 1

elif nav_option == "Mock Interview":
    st.header("🎥 Mock Interview")
    schedule_mock_interview()

elif nav_option == "Track Progress":
    track_progress()

elif nav_option == "Connect with Resources":
    connect_resources()

# Footer
st.markdown("""
    <div class="footer">
        <p>&copy; 2024 TechPrep. All rights reserved.</p>
    </div>
    """, unsafe_allow_html=True)