app.py

import streamlit as st
import requests
from langchain_groq import ChatGroq
from streamlit_chat import message
import plotly.express as px
import pandas as pd
import sqlite3
from datetime import datetime, timedelta
import re
import os
import fitz  # PyMuPDF
from bs4 import BeautifulSoup
from streamlit_option_menu import option_menu

# Secrets and API Keys
GROQ_API_KEY = st.secrets["GROQ_API_KEY"]
RAPIDAPI_KEY = st.secrets["RAPIDAPI_KEY"]
YOUTUBE_API_KEY = st.secrets["YOUTUBE_API_KEY"]
THE_MUSE_API_KEY = st.secrets.get("THE_MUSE_API_KEY", "") 
BLS_API_KEY = st.secrets.get("BLS_API_KEY", "")  

llm = ChatGroq(
    temperature=0,
    groq_api_key=GROQ_API_KEY,
    model_name="llama-3.1-70b-versatile"
)

# -------------------------------
# PDF and HTML Extraction Functions
# -------------------------------
@st.cache_data(ttl=3600)
def extract_text_from_pdf(pdf_file):
    """
    Extracts text from an uploaded PDF file.
    """
    text = ""
    try:
        with fitz.open(stream=pdf_file.read(), filetype="pdf") as doc:
            for page in doc:
                text += page.get_text()
        return text
    except Exception as e:
        st.error(f"Error extracting text from PDF: {e}")
        return ""

@st.cache_data(ttl=3600)
def extract_job_description(job_link):
    """
    Fetches and extracts job description text from a given URL.
    """
    try:
        headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}
        response = requests.get(job_link, headers=headers)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        job_description = soup.get_text(separator='\n')
        return job_description.strip()
    except Exception as e:
        st.error(f"Error fetching job description: {e}")
        return ""

@st.cache_data(ttl=3600)
def extract_requirements(job_description):
    """
    Uses Groq to extract job requirements from the job description.
    """
    prompt = f"""
    The following is a job description:

    {job_description}

    Extract the list of job requirements, qualifications, and skills from the job description. Provide them as a numbered list.

    Requirements:
    """
    try:
        response = llm.invoke(prompt)
        return response.content.strip()
    except Exception as e:
        st.error(f"Error extracting requirements: {e}")
        return ""

# -------------------------------
# Email and Cover Letter Generation
# -------------------------------
@st.cache_data(ttl=3600)
def generate_email(job_description, requirements, resume_text):
    """
    Generates a personalized cold email using Groq.
    """
    prompt = f"""
    You are Adithya S Nair, a recent Computer Science graduate specializing in Artificial Intelligence and Machine Learning. Craft a concise and professional cold email to a potential employer based on the following information:

    **Job Description:**
    {job_description}

    **Extracted Requirements:**
    {requirements}

    **Your Resume:**
    {resume_text}

    **Email Requirements:**
    - Introduction: Briefly introduce yourself and mention the specific job you are applying for.
    - Body: Highlight your relevant skills, projects, internships, and leadership experiences.
    - Value Proposition: Explain how your fresh perspective can add value to the company.
    - Closing: Express enthusiasm and request an interview.
    """
    try:
        response = llm.invoke(prompt)
        return response.content.strip()
    except Exception as e:
        st.error(f"Error generating email: {e}")
        return ""

@st.cache_data(ttl=3600)
def generate_cover_letter(job_description, requirements, resume_text):
    """
    Generates a personalized cover letter using Groq.
    """
    prompt = f"""
    You are Adithya S Nair, a recent Computer Science graduate specializing in Artificial Intelligence and Machine Learning. Compose a professional cover letter based on the following information:

    **Job Description:**
    {job_description}

    **Extracted Requirements:**
    {requirements}

    **Your Resume:**
    {resume_text}

    **Cover Letter Requirements:**
    1. Greeting: Address the hiring manager.
    2. Introduction: Mention the position and your enthusiasm.
    3. Body: Highlight skills, experiences, and relevant projects.
    4. Value Proposition: Explain how you can contribute to the company.
    5. Conclusion: Express interest in an interview and thank the reader.
    """
    try:
        response = llm.invoke(prompt)
        return response.content.strip()
    except Exception as e:
        st.error(f"Error generating cover letter: {e}")
        return ""

# -------------------------------
# Resume Analysis Functions
# -------------------------------
@st.cache_data(ttl=3600)
def extract_skills(text):
    """
    Extracts a list of skills from the resume text using Groq.
    """
    prompt = f"""
    Extract a comprehensive list of technical and soft skills from the following resume text. Provide the skills as a comma-separated list.

    Resume Text:
    {text}

    Skills:
    """
    try:
        response = llm.invoke(prompt)
        skills = response.content.strip()
        skills_list = [skill.strip() for skill in re.split(',|\n', skills) if skill.strip()]
        return skills_list
    except Exception as e:
        st.error(f"Error extracting skills: {e}")
        return []

@st.cache_data(ttl=3600)
def suggest_keywords(resume_text, job_description=None):
    """
    Suggests additional relevant keywords for ATS optimization.
    """
    prompt = f"""
    Analyze the following resume text and suggest additional relevant keywords that can enhance its compatibility with Applicant Tracking Systems (ATS). If a job description is provided, tailor the keywords accordingly.

    Resume Text:
    {resume_text}

    Job Description:
    {job_description if job_description else "N/A"}

    Suggested Keywords:
    """
    try:
        response = llm.invoke(prompt)
        keywords = response.content.strip()
        keywords_list = [keyword.strip() for keyword in re.split(',|\n', keywords) if keyword.strip()]
        return keywords_list
    except Exception as e:
        st.error(f"Error suggesting keywords: {e}")
        return []

def create_skill_distribution_chart(skills):
    """
    Creates a bar chart showing the distribution of skills.
    """
    skill_counts = {}
    for skill in skills:
        skill_counts[skill] = skill_counts.get(skill, 0) + 1
    df = pd.DataFrame(list(skill_counts.items()), columns=['Skill', 'Count'])
    fig = px.bar(df, x='Skill', y='Count', title='Skill Distribution')
    return fig

def create_experience_timeline(resume_text):
    """
    Creates an experience timeline from the resume text.
    """
    prompt = f"""
    From the following resume text, extract the job titles, companies, and durations of employment. Provide the information in a table format with columns: Job Title, Company, Duration (in years).

    Resume Text:
    {resume_text}

    Table:
    """
    try:
        response = llm.invoke(prompt)
        table_text = response.content.strip()
        data = []
        for line in table_text.split('\n'):
            if line.strip() and not line.lower().startswith("job title"):
                parts = line.split('|')
                if len(parts) == 3:
                    job_title = parts[0].strip()
                    company = parts[1].strip()
                    duration = parts[2].strip()
                    duration_years = parse_duration(duration)
                    data.append({"Job Title": job_title, "Company": company, "Duration (years)": duration_years})
        df = pd.DataFrame(data)
        if not df.empty:
            df['Start Year'] = df['Duration (years)'].cumsum() - df['Duration (years)']
            df['End Year'] = df['Duration (years)'].cumsum()
            fig = px.timeline(df, x_start="Start Year", x_end="End Year", y="Job Title", color="Company", title="Experience Timeline")
            fig.update_yaxes(categoryorder="total ascending")
            return fig
        else:
            return None
    except Exception as e:
        st.error(f"Error creating experience timeline: {e}")
        return None

def parse_duration(duration_str):
    """
    Parses duration strings like '2 years' or '6 months' into float years.
    """
    try:
        if 'year' in duration_str.lower():
            years = float(re.findall(r'\d+\.?\d*', duration_str)[0])
            return years
        elif 'month' in duration_str.lower():
            months = float(re.findall(r'\d+\.?\d*', duration_str)[0])
            return months / 12
        else:
            return 0
    except:
        return 0

# -------------------------------
# Job API Integration Functions
# -------------------------------
@st.cache_data(ttl=86400)
def fetch_remotive_jobs_api(job_title, location=None, category=None, remote=True, max_results=50):
    """
    Fetches job listings from Remotive API.
    """
    base_url = "https://remotive.com/api/remote-jobs"
    params = {"search": job_title, "limit": max_results}
    if category:
        params["category"] = category
    try:
        response = requests.get(base_url, params=params)
        response.raise_for_status()
        jobs = response.json().get("jobs", [])
        if remote:
            jobs = [job for job in jobs if job.get("candidate_required_location") == "Worldwide" or job.get("remote") == True]
        return jobs
    except requests.exceptions.RequestException as e:
        st.error(f"Error fetching jobs from Remotive: {e}")
        return []

@st.cache_data(ttl=86400)
def fetch_muse_jobs_api(job_title, location=None, category=None, max_results=50):
    """
    Fetches job listings from The Muse API.
    """
    base_url = "https://www.themuse.com/api/public/jobs"
    headers = {"Content-Type": "application/json"}
    params = {"page": 1, "per_page": max_results, "category": category, "location": location, "company": None}
    try:
        response = requests.get(base_url, params=params, headers=headers)
        response.raise_for_status()
        jobs = response.json().get("results", [])
        filtered_jobs = [job for job in jobs if job_title.lower() in job.get("name", "").lower()]
        return filtered_jobs
    except requests.exceptions.RequestException as e:
        st.error(f"Error fetching jobs from The Muse: {e}")
        return []

@st.cache_data(ttl=86400)
def fetch_indeed_jobs_list_api(job_title, location="United States", distance="1.0", language="en_GB", remoteOnly="false", datePosted="month", employmentTypes="fulltime;parttime;intern;contractor", index=0, page_size=10):
    """
    Fetches a list of job IDs from Indeed API.
    """
    url = "https://jobs-api14.p.rapidapi.com/list"
    querystring = {
        "query": job_title,
        "location": location,
        "distance": distance,
        "language": language,
        "remoteOnly": remoteOnly,
        "datePosted": datePosted,
        "employmentTypes": employmentTypes,
        "index": str(index),
        "page_size": str(page_size)
    }
    headers = {"x-rapidapi-key": RAPIDAPI_KEY, "x-rapidapi-host": "jobs-api14.p.rapidapi.com"}
    try:
        response = requests.get(url, headers=headers, params=querystring)
        response.raise_for_status()
        data = response.json()
        job_ids = [job["id"] for job in data.get("jobs", [])]
        return job_ids
    except requests.exceptions.RequestException as e:
        st.error(f"Error fetching job IDs from Indeed: {e}")
        return []

@st.cache_data(ttl=86400)
def fetch_indeed_job_details_api(job_id, language="en_GB"):
    """
    Fetches job details from Indeed API.
    """
    url = "https://jobs-api14.p.rapidapi.com/get"
    querystring = {"id": job_id, "language": language}
    headers = {"x-rapidapi-key": RAPIDAPI_KEY, "x-rapidapi-host": "jobs-api14.p.rapidapi.com"}
    try:
        response = requests.get(url, headers=headers, params=querystring)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        st.error(f"Error fetching job details from Indeed: {e}")
        return {}

def recommend_indeed_jobs(user_skills, user_preferences):
    """
    Recommends jobs from Indeed API based on user skills and preferences.
    """
    job_title = user_preferences.get("job_title", "")
    location = user_preferences.get("location", "United States")
    category = user_preferences.get("category", "")
    language = "en_GB"

    job_ids = fetch_indeed_jobs_list_api(job_title, location=location, category=category, page_size=5)
    recommended_jobs = []
    api_calls_needed = len(job_ids)

    if not can_make_api_calls(api_calls_needed):
        st.error("❌ You have reached your monthly API request limit. Please try again later.")
        return []

    for job_id in job_ids:
        job_details = fetch_indeed_job_details_api(job_id, language=language)
        if job_details and not job_details.get("hasError", True):
            job_description = job_details.get("description", "").lower()
            match_score = sum(skill.lower() in job_description for skill in user_skills)
            if match_score > 0:
                recommended_jobs.append((match_score, job_details))
                decrement_api_calls(1)
    
    recommended_jobs.sort(reverse=True, key=lambda x: x[0])
    return [job for score, job in recommended_jobs[:10]]

def recommend_jobs(user_skills, user_preferences):
    """
    Combines job recommendations from Remotive, The Muse, and Indeed.
    """
    remotive_jobs = fetch_remotive_jobs_api(user_preferences.get("job_title", ""), user_preferences.get("location"), user_preferences.get("category"))
    muse_jobs = fetch_muse_jobs_api(user_preferences.get("job_title", ""), user_preferences.get("location"), user_preferences.get("category"))
    indeed_jobs = recommend_indeed_jobs(user_skills, user_preferences)

    combined_jobs = remotive_jobs + muse_jobs + indeed_jobs
    unique_jobs = {}
    for job in combined_jobs:
        url = job.get("url") or job.get("redirect_url") or job.get("url_standard")
        if url and url not in unique_jobs:
            unique_jobs[url] = job
    return list(unique_jobs.values())

# -------------------------------
# API Usage Counter Functions
# -------------------------------
def init_api_usage_db():
    """
    Initializes the SQLite database and creates the api_usage table if it doesn't exist.
    """
    conn = sqlite3.connect('applications.db')
    c = conn.cursor()
    c.execute('''
        CREATE TABLE IF NOT EXISTS api_usage (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            count INTEGER,
            last_reset DATE
        )
    ''')
    c.execute('SELECT COUNT(*) FROM api_usage')
    if c.fetchone()[0] == 0:
        c.execute('INSERT INTO api_usage (count, last_reset) VALUES (?, ?)', (25, datetime.now().date()))
    conn.commit()
    conn.close()

def get_api_usage():
    """
    Retrieves the current API usage count and last reset date.
    """
    conn = sqlite3.connect('applications.db')
    c = conn.cursor()
    c.execute('SELECT count, last_reset FROM api_usage WHERE id = 1')
    row = c.fetchone()
    conn.close()
    if row:
        return row[0], datetime.strptime(row[1], "%Y-%m-%d").date()
    else:
        return 25, datetime.now().date()

def reset_api_usage():
    """
    Resets the API usage count to 25.
    """
    conn = sqlite3.connect('applications.db')
    c = conn.cursor()
    c.execute('UPDATE api_usage SET count = ?, last_reset = ? WHERE id = 1', (25, datetime.now().date()))
    conn.commit()
    conn.close()

def can_make_api_calls(requests_needed):
    """
    Checks if there are enough API calls remaining.
    """
    count, last_reset = get_api_usage()
    today = datetime.now().date()
    if today >= last_reset + timedelta(days=30):
        reset_api_usage()
        count, last_reset = get_api_usage()
    return count >= requests_needed

def decrement_api_calls(requests_used):
    """
    Decrements the API usage count.
    """
    conn = sqlite3.connect('applications.db')
    c = conn.cursor()
    c.execute('SELECT count FROM api_usage WHERE id = 1')
    row = c.fetchone()
    if row:
        new_count = max(row[0] - requests_used, 0)
        c.execute('UPDATE api_usage SET count = ? WHERE id = 1', (new_count,))
        conn.commit()
    conn.close()

# -------------------------------
# Application Tracking Functions
# -------------------------------
def init_db():
    """
    Initializes the SQLite database and creates the applications table.
    """
    conn = sqlite3.connect('applications.db')
    c = conn.cursor()
    c.execute('''
        CREATE TABLE IF NOT EXISTS applications (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            job_title TEXT,
            company TEXT,
            application_date TEXT,
            status TEXT,
            deadline TEXT,
            notes TEXT,
            job_description TEXT,
            resume_text TEXT,
            skills TEXT
        )
    ''')
    conn.commit()
    conn.close()

def add_application(job_title, company, application_date, status, deadline, notes, job_description, resume_text, skills):
    """
    Adds a new job application to the database.
    """
    conn = sqlite3.connect('applications.db')
    c = conn.cursor()
    c.execute('''
        INSERT INTO applications (job_title, company, application_date, status, deadline, notes, job_description, resume_text, skills)
        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
    ''', (job_title, company, application_date, status, deadline, notes, job_description, resume_text, ', '.join(skills)))
    conn.commit()
    conn.close()

def fetch_applications():
    """
    Fetches all applications from the database.
    """
    conn = sqlite3.connect('applications.db')
    c = conn.cursor()
    c.execute('SELECT * FROM applications')
    data = c.fetchall()
    conn.close()
    applications = []
    for app in data:
        applications.append({
            "ID": app[0],
            "Job Title": app[1],
            "Company": app[2],
            "Application Date": app[3],
            "Status": app[4],
            "Deadline": app[5],
            "Notes": app[6],
            "Job Description": app[7],
            "Resume Text": app[8],
            "Skills": app[9].split(', ') if app[9] else []
        })
    return applications

def update_application_status(app_id, new_status):
    """
    Updates the status of an application.
    """
    conn = sqlite3.connect('applications.db')
    c = conn.cursor()
    c.execute('UPDATE applications SET status = ? WHERE id = ?', (new_status, app_id))
    conn.commit()
    conn.close()

def delete_application(app_id):
    """
    Deletes an application from the database.
    """
    conn = sqlite3.connect('applications.db')
    c = conn.cursor()
    c.execute('DELETE FROM applications WHERE id = ?', (app_id,))
    conn.commit()
    conn.close()

# -------------------------------
# Learning Path Generation
# -------------------------------
@st.cache_data(ttl=86400)
def generate_learning_path(career_goal, current_skills):
    """
    Generates a personalized learning path using Groq.
    """
    prompt = f"""
    Based on the following career goal and current skills, create a personalized learning path that includes recommended courses, projects, and milestones.

    **Career Goal:**
    {career_goal}

    **Current Skills:**
    {current_skills}

    **Learning Path:**
    """
    try:
        response = llm.invoke(prompt)
        return response.content.strip()
    except Exception as e:
        st.error(f"Error generating learning path: {e}")
        return ""

# -------------------------------
# YouTube Video Search and Embed Functions
# -------------------------------
@st.cache_data(ttl=86400)
def search_youtube_videos(query, max_results=2, video_duration="long"):
    """
    Searches YouTube for videos matching the query.
    """
    search_url = "https://www.googleapis.com/youtube/v3/search"
    params = {
        "part": "snippet",
        "q": query,
        "type": "video",
        "maxResults": max_results,
        "videoDuration": video_duration,
        "key": YOUTUBE_API_KEY
    }
    try:
        response = requests.get(search_url, params=params)
        response.raise_for_status()
        results = response.json().get("items", [])
        video_urls = [f"https://www.youtube.com/watch?v={item['id']['videoId']}" for item in results]
        return video_urls
    except requests.exceptions.RequestException as e:
        st.error(f"❌ Error fetching YouTube videos: {e}")
        return []

def embed_youtube_videos(video_urls, module_name):
    """
    Embeds YouTube videos.
    """
    for url in video_urls:
        st.video(url)

# -------------------------------
# Application Modules (Pages)
# -------------------------------
def email_generator_page():
    st.header("📧 Automated Email Generator")
    st.write("Generate personalized cold emails based on job postings and your resume.")

    col1, col2 = st.columns(2)
    with col1:
        job_link = st.text_input("🔗 Enter the job link:")
    with col2:
        uploaded_file = st.file_uploader("📄 Upload your resume (PDF):", type="pdf")

    if st.button("Generate Email"):
        if not job_link:
            st.error("Please enter a job link.")
            return
        if not uploaded_file:
            st.error("Please upload your resume.")
            return
        with st.spinner("Processing..."):
            job_description = extract_job_description(job_link)
            if not job_description:
                st.error("Failed to extract job description.")
                return
            requirements = extract_requirements(job_description)
            if not requirements:
                st.error("Failed to extract requirements.")
                return
            resume_text = extract_text_from_pdf(uploaded_file)
            if not resume_text:
                st.error("Failed to extract text from resume.")
                return
            email_text = generate_email(job_description, requirements, resume_text)
            if email_text:
                st.subheader("📨 Generated Email:")
                st.write(email_text)
                st.download_button("Download Email", data=email_text, file_name="generated_email.txt", mime="text/plain")
            else:
                st.error("Failed to generate email.")

def cover_letter_generator_page():
    st.header("📝 Automated Cover Letter Generator")
    st.write("Generate personalized cover letters based on job postings and your resume.")

    col1, col2 = st.columns(2)
    with col1:
        job_link = st.text_input("🔗 Enter the job link:")
    with col2:
        uploaded_file = st.file_uploader("📄 Upload your resume (PDF):", type="pdf")

    if st.button("Generate Cover Letter"):
        if not job_link:
            st.error("Please enter a job link.")
            return
        if not uploaded_file:
            st.error("Please upload your resume.")
            return
        with st.spinner("Processing..."):
            job_description = extract_job_description(job_link)
            if not job_description:
                st.error("Failed to extract job description.")
                return
            requirements = extract_requirements(job_description)
            if not requirements:
                st.error("Failed to extract requirements.")
                return
            resume_text = extract_text_from_pdf(uploaded_file)
            if not resume_text:
                st.error("Failed to extract text from resume.")
                return
            cover_letter = generate_cover_letter(job_description, requirements, resume_text)
            if cover_letter:
                st.subheader("📝 Generated Cover Letter:")
                st.write(cover_letter)
                st.download_button("Download Cover Letter", data=cover_letter, file_name="generated_cover_letter.txt", mime="text/plain")
            else:
                st.error("Failed to generate cover letter.")

def resume_analysis_page():
    st.header("📄 Resume Analysis and Optimization")
    st.write("Enhance your resume by extracting key information, suggestions, and visual analytics.")
    
    uploaded_file = st.file_uploader("📂 Upload your resume (PDF):", type="pdf")
    if uploaded_file:
        resume_text = extract_text_from_pdf(uploaded_file)
        if resume_text:
            st.success("✅ Resume uploaded successfully!")
            st.subheader("🔍 Extracted Information")
            tabs = st.tabs(["💼 Skills", "🔑 Suggested Keywords"])
            with tabs[0]:
                skills = extract_skills(resume_text)
                if skills:
                    st.markdown("**Identified Skills:**")
                    cols = st.columns(4)
                    for idx, skill in enumerate(skills, 1):
                        cols[idx % 4].write(f"- {skill}")
                else:
                    st.info("No skills extracted.")
            with tabs[1]:
                keywords = suggest_keywords(resume_text)
                if keywords:
                    st.markdown("**Suggested Keywords for ATS Optimization:**")
                    cols = st.columns(4)
                    for idx, keyword in enumerate(keywords, 1):
                        cols[idx % 4].write(f"- {keyword}")
                else:
                    st.info("No keywords suggested.")
            st.subheader("🛠️ Optimization Suggestions")
            st.markdown("""
            - **Keyword Optimization:** Incorporate suggested keywords.
            - **Highlight Relevant Sections:** Emphasize skills that match job requirements.
            - **Consistent Formatting:** Ensure readability and structure.
            """)
            st.subheader("📊 Visual Resume Analytics")
            viz_col1, viz_col2 = st.columns(2)
            with viz_col1:
                if skills:
                    st.markdown("**Skill Distribution:**")
                    fig_skills = create_skill_distribution_chart(skills)
                    st.plotly_chart(fig_skills, use_container_width=True)
                else:
                    st.info("No skills to display.")
            with viz_col2:
                fig_experience = create_experience_timeline(resume_text)
                if fig_experience:
                    st.markdown("**Experience Timeline:**")
                    st.plotly_chart(fig_experience, use_container_width=True)
                else:
                    st.info("Not enough data to generate an experience timeline.")
            st.subheader("💾 Save Resume Analysis")
            if st.button("Save Resume Analysis"):
                add_application(
                    job_title="N/A",
                    company="N/A",
                    application_date=datetime.now().strftime("%Y-%m-%d"),
                    status="N/A",
                    deadline="N/A",
                    notes="Resume Analysis",
                    job_description="N/A",
                    resume_text=resume_text,
                    skills=skills
                )
                st.success("✅ Resume analysis saved successfully!")
        else:
            st.error("❌ Failed to extract text from resume.")

def application_tracking_dashboard():
    st.header("📋 Application Tracking Dashboard")
    init_db()
    init_api_usage_db()
    
    st.subheader("➕ Add New Application")
    with st.form("add_application"):
        job_title = st.text_input("🖇️ Job Title")
        company = st.text_input("🏢 Company")
        application_date = st.date_input("📅 Application Date", datetime.today())
        status = st.selectbox("📈 Status", ["Applied", "Interviewing", "Offered", "Rejected"])
        deadline = st.date_input("⏰ Application Deadline", datetime.today() + timedelta(days=30))
        notes = st.text_area("📝 Notes")
        uploaded_file = st.file_uploader("📂 Upload Job Description (PDF)", type="pdf")
        uploaded_resume = st.file_uploader("📄 Upload Resume (PDF)", type="pdf")
        submitted = st.form_submit_button("➕ Add Application")
        if submitted:
            job_description = extract_text_from_pdf(uploaded_file) if uploaded_file else ""
            if uploaded_resume:
                resume_text = extract_text_from_pdf(uploaded_resume)
                skills = extract_skills(resume_text)
            else:
                resume_text = ""
                skills = []
            add_application(
                job_title=job_title,
                company=company,
                application_date=application_date.strftime("%Y-%m-%d"),
                status=status,
                deadline=deadline.strftime("%Y-%m-%d"),
                notes=notes,
                job_description=job_description,
                resume_text=resume_text,
                skills=skills
            )
            st.success("✅ Application added successfully!")
    
    st.subheader("📊 Your Applications")
    applications = fetch_applications()
    if applications:
        df = pd.DataFrame(applications)
        df = df.drop(columns=["Job Description", "Resume Text", "Skills"])
        st.dataframe(df)
        csv = df.to_csv(index=False).encode('utf-8')
        st.download_button("💾 Download Applications as CSV", data=csv, file_name='applications.csv', mime='text/csv')
        st.subheader("📥 Import Applications")
        uploaded_csv = st.file_uploader("📁 Upload a CSV file", type="csv")
        if uploaded_csv:
            try:
                imported_df = pd.read_csv(uploaded_csv)
                required_columns = {"Job Title", "Company", "Application Date", "Status", "Deadline", "Notes"}
                if not required_columns.issubset(imported_df.columns):
                    st.error("❌ Uploaded CSV is missing required columns.")
                else:
                    for _, row in imported_df.iterrows():
                        add_application(
                            job_title=row.get("Job Title", "N/A"),
                            company=row.get("Company", "N/A"),
                            application_date=row.get("Application Date", datetime.now().strftime("%Y-%m-%d")),
                            status=row.get("Status", "Applied"),
                            deadline=row.get("Deadline", ""),
                            notes=row.get("Notes", ""),
                            job_description=row.get("Job Description", ""),
                            resume_text=row.get("Resume Text", ""),
                            skills=row.get("Skills", "").split(', ') if row.get("Skills") else []
                        )
                    st.success("✅ Applications imported successfully!")
            except Exception as e:
                st.error(f"❌ Error importing applications: {e}")
        for app in applications:
            with st.expander(f"{app['Job Title']} at {app['Company']}"):
                st.write(f"**📅 Application Date:** {app['Application Date']}")
                st.write(f"**⏰ Deadline:** {app['Deadline']}")
                st.write(f"**📈 Status:** {app['Status']}")
                st.write(f"**📝 Notes:** {app['Notes']}")
                new_status = st.selectbox("🔄 Update Status:", ["Applied", "Interviewing", "Offered", "Rejected"], key=f"status_{app['ID']}")
                if st.button("🔁 Update Status", key=f"update_{app['ID']}"):
                    update_application_status(app['ID'], new_status)
                    st.success("✅ Status updated successfully!")
                if st.button("🗑️ Delete Application", key=f"delete_{app['ID']}"):
                    delete_application(app['ID'])
                    st.success("✅ Application deleted successfully!")
    else:
        st.write("ℹ️ No applications found.")

def job_recommendations_module():
    st.header("🔍 Job Matching & Recommendations")
    st.write("Discover job opportunities tailored to your skills and preferences.")
    st.subheader("🎯 Set Your Preferences")
    with st.form("preferences_form"):
        job_title = st.text_input("🔍 Desired Job Title", placeholder="e.g., Data Scientist")
        location = st.text_input("📍 Preferred Location", placeholder="e.g., New York, USA or Remote")
        category = st.selectbox("📂 Job Category", ["", "Engineering", "Marketing", "Design", "Sales", "Finance", "Healthcare", "Education", "Other"])
        user_skills_input = st.text_input("💡 Your Skills (comma-separated)", placeholder="e.g., Python, Machine Learning, SQL")
        submitted = st.form_submit_button("🚀 Get Recommendations")
        if submitted:
            if not job_title or not user_skills_input:
                st.error("❌ Please enter both job title and your skills.")
                return
            user_skills = [skill.strip() for skill in user_skills_input.split(",") if skill.strip()]
            user_preferences = {"job_title": job_title, "location": location, "category": category}
            with st.spinner("🔄 Fetching job recommendations..."):
                recommended_jobs = recommend_jobs(user_skills, user_preferences)
                if recommended_jobs:
                    st.subheader("💼 Recommended Jobs:")
                    for idx, job in enumerate(recommended_jobs, 1):
                        job_title_display = job.get("title") or job.get("name") or job.get("jobTitle")
                        company_display = job.get("company", {}).get("name") or job.get("company_name") or job.get("employer", {}).get("name")
                        location_display = job.get("candidate_required_location") or job.get("location") or job.get("country")
                        job_url = job.get("url") or job.get("redirect_url") or job.get("url_standard")
                        st.markdown(f"### {idx}. {job_title_display}")
                        st.markdown(f"**🏢 Company:** {company_display}")
                        st.markdown(f"**📍 Location:** {location_display}")
                        st.markdown(f"**🔗 Job URL:** [Apply Here]({job_url})")
                        st.write("---")
                else:
                    st.info("ℹ️ No job recommendations found based on your criteria.")

def interview_preparation_module():
    st.header("🎤 Interview Preparation")
    st.write("Prepare for your interviews with tailored mock questions and answers.")
    col1, col2 = st.columns(2)
    with col1:
        job_title = st.text_input("🔍 Enter the job title you're applying for:")
    with col2:
        company = st.text_input("🏢 Enter the company name:")
    if st.button("🎯 Generate Mock Interview Questions"):
        if not job_title or not company:
            st.error("❌ Please enter both job title and company name.")
            return
        with st.spinner("⏳ Generating questions..."):
            prompt = f"""
            Generate a list of 50 interview questions along with their answers for the position of {job_title} at {company}. Each question should be followed by a concise and professional answer.
            """
            try:
                qa_text = llm.invoke(prompt).content.strip()
                qa_pairs = qa_text.split('\n\n')
                st.subheader("🗣️ Mock Interview Questions and Answers:")
                for idx, qa in enumerate(qa_pairs, 1):
                    if qa.strip():
                        parts = qa.split('\n', 1)
                        if len(parts) == 2:
                            question = parts[0].strip()
                            answer = parts[1].strip()
                            st.markdown(f"**Q{idx}: {question}**")
                            st.markdown(f"**A:** {answer}")
                            st.write("---")
            except Exception as e:
                st.error(f"❌ Error generating interview questions: {e}")

def personalized_learning_paths_module():
    st.header("📚 Personalized Learning Paths")
    st.write("Receive tailored learning plans to help you achieve your career goals, complemented with curated video resources.")
    col1, col2 = st.columns(2)
    with col1:
        career_goal = st.text_input("🎯 Enter your career goal (e.g., Data Scientist):")
    with col2:
        current_skills = st.text_input("💡 Enter your current skills (comma-separated):")
    if st.button("🚀 Generate Learning Path"):
        if not career_goal or not current_skills:
            st.error("❌ Please enter both career goal and current skills.")
            return
        with st.spinner("🔄 Generating your personalized learning path..."):
            learning_path = generate_learning_path(career_goal, current_skills)
            if learning_path:
                st.subheader("📜 Your Personalized Learning Path:")
                st.write(learning_path)
                modules = re.split(r'\d+\.\s+', learning_path)
                modules = [module.strip() for module in modules if module.strip()]
                st.subheader("📹 Recommended YouTube Videos for Each Module:")
                for module in modules:
                    video_urls = search_youtube_videos(query=module, max_results=2, video_duration="long")
                    if video_urls:
                        st.markdown(f"### {module}")
                        embed_youtube_videos(video_urls, module)
                    else:
                        st.write(f"No videos found for **{module}**.")
            else:
                st.error("❌ Failed to generate learning path.")

def networking_opportunities_module():
    st.header("🤝 Networking Opportunities")
    st.write("Expand your professional network by connecting with relevant industry peers and groups.")
    col1, col2 = st.columns(2)
    with col1:
        user_skills = st.text_input("💡 Enter your key skills (comma-separated):")
    with col2:
        industry = st.text_input("🏭 Enter your industry (e.g., Technology):")
    if st.button("🔍 Find Networking Opportunities"):
        if not user_skills or not industry:
            st.error("❌ Please enter both key skills and industry.")
            return
        with st.spinner("🔄 Fetching networking opportunities..."):
            prompt = f"""
            Based on the following skills: {user_skills}, and industry: {industry}, suggest relevant LinkedIn groups, professional organizations, and industry events for networking.
            """
            try:
                suggestions = llm.invoke(prompt).content.strip()
                st.subheader("🔗 Recommended Networking Groups and Events:")
                st.write(suggestions)
            except Exception as e:
                st.error(f"❌ Error fetching networking opportunities: {e}")

def feedback_and_improvement_module():
    st.header("🗣️ Feedback and Continuous Improvement")
    st.write("We value your feedback! Let us know how we can improve your experience.")
    with st.form("feedback_form"):
        name = st.text_input("👤 Your Name")
        email = st.text_input("📧 Your Email")
        feedback_type = st.selectbox("📂 Type of Feedback", ["Bug Report", "Feature Request", "General Feedback"])
        feedback = st.text_area("📝 Your Feedback")
        submitted = st.form_submit_button("✅ Submit")
        if submitted:
            if not name or not email or not feedback:
                st.error("❌ Please fill in all the fields.")
            else:
                # You can store the feedback in a database or send via email
                st.success("✅ Thank you for your feedback!")

def resource_library_page():
    st.header("📚 Resource Library")
    st.write("Access a collection of templates and guides to enhance your job search.")
    resources = [
        {"title": "Resume Template", "description": "A professional resume template in DOCX format.", "file": "./resume_template.docx"},
        {"title": "Cover Letter Template", "description": "A customizable cover letter template.", "file": "./cover_letter_template.docx"},
        {"title": "Job Application Checklist", "description": "A checklist to ensure you cover all steps.", "file": "./application_checklist.pdf"}
    ]
    for resource in resources:
        st.markdown(f"### {resource['title']}")
        st.write(resource['description'])
        try:
            with open(resource['file'], "rb") as file:
                st.download_button("⬇️ Download", data=file, file_name=os.path.basename(resource['file']), mime="application/octet-stream")
        except FileNotFoundError:
            st.error(f"❌ File {resource['file']} not found. Please ensure the file is in the correct directory.")
        st.write("---")

def chatbot_support_page():
    st.header("🤖 AI-Powered Chatbot Support")
    st.write("Have questions or need assistance? Chat with our AI-powered assistant!")
    if 'chat_history' not in st.session_state:
        st.session_state['chat_history'] = []
    user_input = st.text_input("🗨️ You:", key="user_input")
    if st.button("Send"):
        if user_input:
            st.session_state['chat_history'].append({"message": user_input, "is_user": True})
            prompt = f"""
            You are a helpful assistant for a Job Application Assistant app. Answer the user's query based on the following context:

            {user_input}
            """
            try:
                response = llm.invoke(prompt)
                assistant_message = response.content.strip()
                st.session_state['chat_history'].append({"message": assistant_message, "is_user": False})
            except Exception as e:
                error_message = "❌ Sorry, I encountered an error while processing your request."
                st.session_state['chat_history'].append({"message": error_message, "is_user": False})
                st.error(f"❌ Error in chatbot: {e}")
    for chat in st.session_state['chat_history']:
        if chat['is_user']:
            message(chat['message'], is_user=True, avatar_style="thumbs")
        else:
            message(chat['message'], is_user=False, avatar_style="bottts")

def help_page():
    st.header("❓ Help & FAQ")
    with st.expander("🛠️ How do I generate a cover letter?"):
        st.write("Navigate to the **Cover Letter Generator** section, enter the job link, upload your resume, and click **Generate Cover Letter**.")
    with st.expander("📋 How do I track my applications?"):
        st.write("Use the **Application Tracking Dashboard** to add and manage your job applications.")
    with st.expander("📄 How can I optimize my resume?"):
        st.write("Upload your resume in the **Resume Analysis** section to extract skills and receive optimization suggestions.")
    with st.expander("📥 How do I import my applications?"):
        st.write("In the **Application Tracking Dashboard**, use the **Import Applications** section to upload a CSV file with the required columns.")
    with st.expander("🗣️ How do I provide feedback?"):
        st.write("Go to the **Feedback** section, fill out the form, and submit your feedback.")

# -------------------------------
# Main Application
# -------------------------------
def main_app():
    st.markdown(
        """
        <style>
        .reportview-container { background-color: #f5f5f5; }
        .sidebar .sidebar-content { background-image: linear-gradient(#2e7bcf, #2e7bcf); color: white; }
        </style>
        """,
        unsafe_allow_html=True
    )
    with st.sidebar:
        selected = option_menu(
            menu_title="📂 Main Menu",
            options=[
                "Email Generator", "Cover Letter Generator", "Resume Analysis",
                "Application Tracking", "Job Recommendations", "Interview Preparation",
                "Personalized Learning Paths", "Networking Opportunities",
                "Feedback", "Resource Library", "Chatbot Support", "Help"
            ],
            icons=[
                "envelope", "file-earmark-text", "file-person", "briefcase",
                "search", "microphone", "book", "people",
                "chat-left-text", "collection", "robot", "question-circle"
            ],
            menu_icon="cast",
            default_index=0,
            styles={
                "container": {"padding": "5!important", "background-color": "#2e7bcf"},
                "icon": {"color": "white", "font-size": "18px"},
                "nav-link": {"font-size": "16px", "text-align": "left", "margin": "0px", "--hover-color": "#6b9eff"},
                "nav-link-selected": {"background-color": "#1e5aab"},
            }
        )
    if selected == "Email Generator":
        email_generator_page()
    elif selected == "Cover Letter Generator":
        cover_letter_generator_page()
    elif selected == "Resume Analysis":
        resume_analysis_page()
    elif selected == "Application Tracking":
        application_tracking_dashboard()
    elif selected == "Job Recommendations":
        job_recommendations_module()
    elif selected == "Interview Preparation":
        interview_preparation_module()
    elif selected == "Personalized Learning Paths":
        personalized_learning_paths_module()
    elif selected == "Networking Opportunities":
        networking_opportunities_module()
    elif selected == "Feedback":
        feedback_and_improvement_module()
    elif selected == "Resource Library":
        resource_library_page()
    elif selected == "Chatbot Support":
        chatbot_support_page()
    elif selected == "Help":
        help_page()

if __name__ == "__main__":
    main_app()