Update app.py

app.py

@@ -17,6 +17,10 @@ from bs4 import BeautifulSoup
 GROQ_API_KEY = st.secrets["GROQ_API_KEY"]
 RAPIDAPI_KEY = st.secrets["RAPIDAPI_KEY"]
 YOUTUBE_API_KEY = st.secrets["YOUTUBE_API_KEY"]
+ADZUNA_APP_ID = st.secrets["ADZUNA_APP_ID"]
+ADZUNA_APP_KEY = st.secrets["ADZUNA_APP_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,
@@ -103,8 +107,6 @@ def generate_email(job_description, requirements, resume_text):
     - **Body:** Highlight your relevant skills, projects, internships, and leadership experiences that align with the job requirements.
     - **Value Proposition:** Explain how your fresh perspective and recent academic knowledge can add value to the company.
     - **Closing:** Express enthusiasm for the opportunity, mention your willingness for an interview, and thank the recipient for their time.
-
-    **Email:**
     """
 
     try:
@@ -141,8 +143,6 @@ def generate_cover_letter(job_description, requirements, resume_text):
     4. **Value Proposition:** Explain how your fresh perspective, recent academic knowledge, and eagerness to learn can contribute to the company's success.
     5. **Conclusion:** End with a strong closing statement expressing your interest in an interview, your availability, and gratitude for the hiring manager’s time and consideration.
     6. **Professional Tone:** Maintain a respectful and professional tone throughout the letter.
-
-    **Cover Letter:**
     """
 
     try:
@@ -317,65 +317,284 @@ def parse_duration(duration_str):
         return 0
 
 # -------------------------------
-# YouTube Data API Integration
+# API Integration Functions
 # -------------------------------
 
+# Remotive API Integration
 @st.cache_data(ttl=86400)  # Cache results for 1 day
-def search_youtube_videos(query, max_results=3, video_duration="long", order="relevance"):
+def fetch_remotive_jobs_api(job_title, location=None, category=None, remote=True, max_results=50):
     """
-    Searches YouTube for videos related to the query with additional filters.
-
+    Fetches job listings from Remotive API based on user preferences.
+    
     Args:
-        query (str): The search query/topic.
-        max_results (int): The maximum number of videos to retrieve.
-        video_duration (str): Duration filter ('any', 'short', 'medium', 'long').
-        order (str): Order of results ('date', 'rating', 'relevance', 'title', 'videoCount', 'viewCount').
-
+        job_title (str): The job title to search for.
+        location (str, optional): The job location. Defaults to None.
+        category (str, optional): The job category. Defaults to None.
+        remote (bool, optional): Whether to fetch remote jobs. Defaults to True.
+        max_results (int, optional): Maximum number of jobs to fetch. Defaults to 50.
+    
     Returns:
-        list: A list of YouTube video URLs.
+        list: A list of job dictionaries.
     """
-    api_key = YOUTUBE_API_KEY
-    search_url = "https://www.googleapis.com/youtube/v3/search"
+    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:
+            # Filter for remote jobs if not already
+            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 []
 
+# The Muse API Integration
+@st.cache_data(ttl=86400)  # Cache results for 1 day
+def fetch_muse_jobs_api(job_title, location=None, category=None, max_results=50):
+    """
+    Fetches job listings from The Muse API based on user preferences.
+    
+    Args:
+        job_title (str): The job title to search for.
+        location (str, optional): The job location. Defaults to None.
+        category (str, optional): The job category. Defaults to None.
+        max_results (int, optional): Maximum number of jobs to fetch. Defaults to 50.
+    
+    Returns:
+        list: A list of job dictionaries.
+    """
+    base_url = "https://www.themuse.com/api/public/jobs"
+    headers = {
+        "Content-Type": "application/json"
+    }
     params = {
-        "part": "snippet",
-        "q": query,
-        "type": "video",
-        "maxResults": max_results,
-        "videoDuration": video_duration,
-        "order": order,
-        "key": api_key
+        "page": 1,
+        "per_page": max_results,
+        "category": category,
+        "location": location,
+        "company": None  # Can be extended based on needs
     }
+    try:
+        response = requests.get(base_url, params=params, headers=headers)
+        response.raise_for_status()
+        jobs = response.json().get("results", [])
+        # Filter based on job title
+        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 []
 
+# Adzuna API Integration
+@st.cache_data(ttl=86400)  # Cache results for 1 day
+def fetch_adzuna_jobs_api(job_title, location="india", category=None, max_results=50):
+    """
+    Fetches job listings from Adzuna API based on user preferences.
+    
+    Args:
+        job_title (str): The job title to search for.
+        location (str, optional): The job location. Defaults to "india".
+        category (str, optional): The job category. Defaults to None.
+        max_results (int, optional): Maximum number of jobs to fetch. Defaults to 50.
+    
+    Returns:
+        list: A list of job dictionaries.
+    """
+    base_url = f"https://api.adzuna.com/v1/api/jobs/{location}/search/1"
+    params = {
+        "app_id": ADZUNA_APP_ID,
+        "app_key": ADZUNA_APP_KEY,
+        "what": job_title,
+        "results_per_page": max_results,
+        "content-type": "application/json"
+    }
+    if category:
+        params["category"] = category
     try:
-        response = requests.get(search_url, params=params)
+        response = requests.get(base_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
+        jobs = response.json().get("results", [])
+        return jobs
     except requests.exceptions.RequestException as e:
-        st.error(f"Error fetching YouTube videos: {e}")
+        st.error(f"Error fetching jobs from Adzuna: {e}")
         return []
 
-def embed_youtube_videos(video_urls, topic):
+def recommend_jobs(user_skills, user_preferences):
+    """
+    Recommends jobs based on user skills and preferences from Remotive API.
+    
+    Args:
+        user_skills (list): List of user's skills.
+        user_preferences (dict): User preferences like job title, location, category.
+    
+    Returns:
+        list: Recommended job listings.
     """
-    Embeds YouTube videos under a specific topic within an expander.
+    job_title = user_preferences.get("job_title", "")
+    location = user_preferences.get("location")
+    category = user_preferences.get("category")
+    
+    jobs = fetch_remotive_jobs_api(job_title, location, category)
+    
+    # Simple matching based on skills appearing in job description
+    recommended_jobs = []
+    for job in jobs:
+        job_description = job.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))
+    
+    # Sort jobs based on match_score
+    recommended_jobs.sort(reverse=True, key=lambda x: x[0])
+    
+    # Return only the job dictionaries
+    return [job for score, job in recommended_jobs[:10]]  # Top 10 recommendations
+
+def recommend_muse_jobs(user_skills, user_preferences):
+    """
+    Recommends jobs from The Muse API based on user skills and preferences.
+    
+    Args:
+        user_skills (list): List of user's skills.
+        user_preferences (dict): User preferences like job title, location, category.
+    
+    Returns:
+        list: Recommended job listings.
+    """
+    job_title = user_preferences.get("job_title", "")
+    location = user_preferences.get("location")
+    category = user_preferences.get("category")
+    
+    jobs = fetch_muse_jobs_api(job_title, location, category)
+    
+    # Simple matching based on skills appearing in job description
+    recommended_jobs = []
+    for job in jobs:
+        job_description = job.get("contents", "").lower()
+        match_score = sum(skill.lower() in job_description for skill in user_skills)
+        if match_score > 0:
+            recommended_jobs.append((match_score, job))
+    
+    # Sort jobs based on match_score
+    recommended_jobs.sort(reverse=True, key=lambda x: x[0])
+    
+    # Return only the job dictionaries
+    return [job for score, job in recommended_jobs[:10]]  # Top 10 recommendations
+
+def recommend_adzuna_jobs(user_skills, user_preferences):
+    """
+    Recommends jobs from Adzuna API based on user skills and preferences.
+    
+    Args:
+        user_skills (list): List of user's skills.
+        user_preferences (dict): User preferences like job title, location, category.
+    
+    Returns:
+        list: Recommended job listings.
+    """
+    job_title = user_preferences.get("job_title", "")
+    location = user_preferences.get("location", "india")
+    category = user_preferences.get("category")
+    
+    jobs = fetch_adzuna_jobs_api(job_title, location, category)
+    
+    # Simple matching based on skills appearing in job description
+    recommended_jobs = []
+    for job in jobs:
+        job_description = job.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))
+    
+    # Sort jobs based on match_score
+    recommended_jobs.sort(reverse=True, key=lambda x: x[0])
+    
+    # Return only the job dictionaries
+    return [job for score, job in recommended_jobs[:10]]  # Top 10 recommendations
+
+# -------------------------------
+# BLS API Integration and Display
+# -------------------------------
 
+@st.cache_data(ttl=86400)  # Cache results for 1 day
+def fetch_bls_data(series_ids, start_year=2020, end_year=datetime.now().year):
+    """
+    Fetches labor market data from the BLS API.
+    
     Args:
-        video_urls (list): List of YouTube video URLs.
-        topic (str): The interview topic.
+        series_ids (list): List of BLS series IDs.
+        start_year (int, optional): Start year for data. Defaults to 2020.
+        end_year (int, optional): End year for data. Defaults to current year.
+    
+    Returns:
+        dict: BLS data response.
+    """
+    bls_url = "https://api.bls.gov/publicAPI/v2/timeseries/data/"
+    headers = {
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "seriesid": series_ids,
+        "startyear": str(start_year),
+        "endyear": str(end_year)
+    }
+    try:
+        response = requests.post(bls_url, json=payload, headers=headers)
+        response.raise_for_status()
+        data = response.json()
+        if data.get("status") == "REQUEST_SUCCEEDED":
+            return data.get("Results", {})
+        else:
+            st.error("BLS API request failed.")
+            return {}
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error fetching data from BLS: {e}")
+        return {}
+
+def display_bls_data(series_id, title):
+    """
+    Processes and displays BLS data with visualizations.
+    
+    Args:
+        series_id (str): BLS series ID.
+        title (str): Title for the visualization.
     """
-    with st.expander(f"Videos for {topic}"):
-        for url in video_urls:
-            st.video(url)
+    data = fetch_bls_data([series_id])
+    if not data:
+        st.info("No data available.")
+        return
+    
+    series_data = data.get("series", [])[0]
+    series_title = series_data.get("title", title)
+    observations = series_data.get("data", [])
+    
+    # Extract year and value
+    years = [int(obs["year"]) for obs in observations]
+    values = [float(obs["value"].replace(',', '')) for obs in observations]
+    
+    df = pd.DataFrame({
+        "Year": years,
+        "Value": values
+    }).sort_values("Year")
+    
+    st.markdown(f"### {series_title}")
+    fig = px.line(df, x="Year", y="Value", title=series_title, markers=True)
+    st.plotly_chart(fig, use_container_width=True)
 
 # -------------------------------
-# Database Functions
+# Application Tracking Database Functions
 # -------------------------------
 
 def init_db():
     """
-    Initializes the SQLite database for application tracking.
+    Initializes the SQLite database and creates the applications table if it doesn't exist.
     """
     conn = sqlite3.connect('applications.db')
     c = conn.cursor()
@@ -398,7 +617,7 @@ def init_db():
 
 def add_application(job_title, company, application_date, status, deadline, notes, job_description, resume_text, skills):
     """
-    Adds a new application to the database.
+    Adds a new job application to the database.
     """
     conn = sqlite3.connect('applications.db')
     c = conn.cursor()
@@ -454,6 +673,11 @@ def delete_application(app_id):
     conn.commit()
     conn.close()
 
+# -------------------------------
+# Learning Path Generation Function
+# -------------------------------
+
+@st.cache_data(ttl=86400)  # Cache results for 1 day
 def generate_learning_path(career_goal, current_skills):
     """
     Generates a personalized learning path using Groq based on career goal and current skills.
@@ -478,12 +702,322 @@ def generate_learning_path(career_goal, current_skills):
         st.error(f"Error generating learning path: {e}")
         return ""
 
+# -------------------------------
+# Visual Resume Analytics Functions
+# -------------------------------
+
+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.
+    """
+    # Extract work experience details using Groq
+    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()
+        # Parse the table_text to create a DataFrame
+        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()
+                    # Convert duration to a float representing years
+                    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:
+            # Create a cumulative duration for timeline
+            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 Recommendations and BLS Integration
+# -------------------------------
+
+# Remotive API Integration
+@st.cache_data(ttl=86400)  # Cache results for 1 day
+def fetch_remotive_jobs_api(job_title, location=None, category=None, remote=True, max_results=50):
+    """
+    Fetches job listings from Remotive API based on user preferences.
+    
+    Args:
+        job_title (str): The job title to search for.
+        location (str, optional): The job location. Defaults to None.
+        category (str, optional): The job category. Defaults to None.
+        remote (bool, optional): Whether to fetch remote jobs. Defaults to True.
+        max_results (int, optional): Maximum number of jobs to fetch. Defaults to 50.
+    
+    Returns:
+        list: A list of job dictionaries.
+    """
+    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:
+            # Filter for remote jobs if not already
+            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 []
+
+# The Muse API Integration
+@st.cache_data(ttl=86400)  # Cache results for 1 day
+def fetch_muse_jobs_api(job_title, location=None, category=None, max_results=50):
+    """
+    Fetches job listings from The Muse API based on user preferences.
+    
+    Args:
+        job_title (str): The job title to search for.
+        location (str, optional): The job location. Defaults to None.
+        category (str, optional): The job category. Defaults to None.
+        max_results (int, optional): Maximum number of jobs to fetch. Defaults to 50.
+    
+    Returns:
+        list: A list of job dictionaries.
+    """
+    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  # Can be extended based on needs
+    }
+    try:
+        response = requests.get(base_url, params=params, headers=headers)
+        response.raise_for_status()
+        jobs = response.json().get("results", [])
+        # Filter based on job title
+        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 []
+
+# Adzuna API Integration
+@st.cache_data(ttl=86400)  # Cache results for 1 day
+def fetch_adzuna_jobs_api(job_title, location="india", category=None, max_results=50):
+    """
+    Fetches job listings from Adzuna API based on user preferences.
+    
+    Args:
+        job_title (str): The job title to search for.
+        location (str, optional): The job location. Defaults to "india".
+        category (str, optional): The job category. Defaults to None.
+        max_results (int, optional): Maximum number of jobs to fetch. Defaults to 50.
+    
+    Returns:
+        list: A list of job dictionaries.
+    """
+    base_url = f"https://api.adzuna.com/v1/api/jobs/{location}/search/1"
+    params = {
+        "app_id": ADZUNA_APP_ID,
+        "app_key": ADZUNA_APP_KEY,
+        "what": job_title,
+        "results_per_page": max_results,
+        "content-type": "application/json"
+    }
+    if category:
+        params["category"] = category
+    try:
+        response = requests.get(base_url, params=params)
+        response.raise_for_status()
+        jobs = response.json().get("results", [])
+        return jobs
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error fetching jobs from Adzuna: {e}")
+        return []
+
+def recommend_jobs(user_skills, user_preferences):
+    """
+    Recommends jobs based on user skills and preferences from Remotive API.
+    
+    Args:
+        user_skills (list): List of user's skills.
+        user_preferences (dict): User preferences like job title, location, category.
+    
+    Returns:
+        list: Recommended job listings.
+    """
+    job_title = user_preferences.get("job_title", "")
+    location = user_preferences.get("location")
+    category = user_preferences.get("category")
+    
+    jobs = fetch_remotive_jobs_api(job_title, location, category)
+    
+    # Simple matching based on skills appearing in job description
+    recommended_jobs = []
+    for job in jobs:
+        job_description = job.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))
+    
+    # Sort jobs based on match_score
+    recommended_jobs.sort(reverse=True, key=lambda x: x[0])
+    
+    # Return only the job dictionaries
+    return [job for score, job in recommended_jobs[:10]]  # Top 10 recommendations
+
+def recommend_muse_jobs(user_skills, user_preferences):
+    """
+    Recommends jobs from The Muse API based on user skills and preferences.
+    
+    Args:
+        user_skills (list): List of user's skills.
+        user_preferences (dict): User preferences like job title, location, category.
+    
+    Returns:
+        list: Recommended job listings.
+    """
+    job_title = user_preferences.get("job_title", "")
+    location = user_preferences.get("location")
+    category = user_preferences.get("category")
+    
+    jobs = fetch_muse_jobs_api(job_title, location, category)
+    
+    # Simple matching based on skills appearing in job description
+    recommended_jobs = []
+    for job in jobs:
+        job_description = job.get("contents", "").lower()
+        match_score = sum(skill.lower() in job_description for skill in user_skills)
+        if match_score > 0:
+            recommended_jobs.append((match_score, job))
+    
+    # Sort jobs based on match_score
+    recommended_jobs.sort(reverse=True, key=lambda x: x[0])
+    
+    # Return only the job dictionaries
+    return [job for score, job in recommended_jobs[:10]]  # Top 10 recommendations
+
+def recommend_adzuna_jobs(user_skills, user_preferences):
+    """
+    Recommends jobs from Adzuna API based on user skills and preferences.
+    
+    Args:
+        user_skills (list): List of user's skills.
+        user_preferences (dict): User preferences like job title, location, category.
+    
+    Returns:
+        list: Recommended job listings.
+    """
+    job_title = user_preferences.get("job_title", "")
+    location = user_preferences.get("location", "india")
+    category = user_preferences.get("category")
+    
+    jobs = fetch_adzuna_jobs_api(job_title, location, category)
+    
+    # Simple matching based on skills appearing in job description
+    recommended_jobs = []
+    for job in jobs:
+        job_description = job.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))
+    
+    # Sort jobs based on match_score
+    recommended_jobs.sort(reverse=True, key=lambda x: x[0])
+    
+    # Return only the job dictionaries
+    return [job for score, job in recommended_jobs[:10]]  # Top 10 recommendations
+
+# -------------------------------
+# Labor Market Insights Module
+# -------------------------------
+
+def labor_market_insights_module():
+    st.header("📈 Labor Market Insights")
+
+    st.write("""
+    Gain valuable insights into the current labor market trends, employment rates, and industry growth to make informed career decisions.
+    """)
+
+    # Define BLS Series IDs based on desired data
+    # Example: Unemployment rate (Series ID: LNS14000000)
+    # Reference: https://www.bls.gov/web/laus/laumstrk.htm
+    unemployment_series_id = "LNS14000000"  # Unemployment Rate
+    employment_series_id = "CEU0000000001"  # Total Employment
+
+    # Display Unemployment Rate
+    display_bls_data(unemployment_series_id, "Unemployment Rate (%)")
+
+    # Display Total Employment
+    display_bls_data(employment_series_id, "Total Employment")
+
+    # Additional Insights
+    st.subheader("💡 Additional Insights")
+    st.write("""
+    - **Industry Growth:** Understanding which industries are growing can help you target your job search effectively.
+    - **Salary Trends:** Keeping an eye on salary trends ensures that you negotiate effectively and align your expectations.
+    - **Geographical Demand:** Some regions may have higher demand for certain roles, guiding your location preferences.
+    """)
+
+    # Fetch and display more BLS data as needed
+
 # -------------------------------
 # Page Functions
 # -------------------------------
 
 def email_generator_page():
-    st.header("Automated Email Generator")
+    st.header("📧 Automated Email Generator")
 
     st.write("""
     Generate personalized cold emails based on job postings and your resume.
@@ -492,9 +1026,9 @@ def email_generator_page():
     # Create two columns for input fields
     col1, col2 = st.columns(2)
     with col1:
-        job_link = st.text_input("Enter the job link:")
+        job_link = st.text_input("🔗 Enter the job link:")
     with col2:
-        uploaded_file = st.file_uploader("Upload your resume (PDF format):", type="pdf")
+        uploaded_file = st.file_uploader("📄 Upload your resume (PDF format):", type="pdf")
 
     if st.button("Generate Email"):
         if not job_link:
@@ -526,7 +1060,7 @@ def email_generator_page():
             # Generate email
             email_text = generate_email(job_description, requirements, resume_text)
             if email_text:
-                st.subheader("Generated Email:")
+                st.subheader("📨 Generated Email:")
                 st.write(email_text)
                 # Provide download option
                 st.download_button(
@@ -539,7 +1073,7 @@ def email_generator_page():
                 st.error("Failed to generate email.")
 
 def cover_letter_generator_page():
-    st.header("Automated Cover Letter Generator")
+    st.header("📝 Automated Cover Letter Generator")
 
     st.write("""
     Generate personalized cover letters based on job postings and your resume.
@@ -548,9 +1082,9 @@ def cover_letter_generator_page():
     # Create two columns for input fields
     col1, col2 = st.columns(2)
     with col1:
-        job_link = st.text_input("Enter the job link:")
+        job_link = st.text_input("🔗 Enter the job link:")
     with col2:
-        uploaded_file = st.file_uploader("Upload your resume (PDF format):", type="pdf")
+        uploaded_file = st.file_uploader("📄 Upload your resume (PDF format):", type="pdf")
 
     if st.button("Generate Cover Letter"):
         if not job_link:
@@ -582,7 +1116,7 @@ def cover_letter_generator_page():
             # Generate cover letter
             cover_letter = generate_cover_letter(job_description, requirements, resume_text)
             if cover_letter:
-                st.subheader("Generated Cover Letter:")
+                st.subheader("📝 Generated Cover Letter:")
                 st.write(cover_letter)
                 # Provide download option
                 st.download_button(
@@ -618,7 +1152,7 @@ def resume_analysis_page():
                 skills = extract_skills(resume_text)
                 if skills:
                     st.markdown("**Identified Skills:**")
-                    # Display skills as tags or bullet points
+                    # Display skills as bullet points in columns
                     cols = st.columns(4)
                     for idx, skill in enumerate(skills, 1):
                         cols[idx % 4].write(f"- {skill}")
@@ -629,7 +1163,7 @@ def resume_analysis_page():
                 keywords = suggest_keywords(resume_text)
                 if keywords:
                     st.markdown("**Suggested Keywords for ATS Optimization:**")
-                    # Display keywords as tags
+                    # Display keywords as bullet points in columns
                     cols = st.columns(4)
                     for idx, keyword in enumerate(keywords, 1):
                         cols[idx % 4].write(f"- {keyword}")
@@ -691,25 +1225,24 @@ def resume_analysis_page():
         else:
             st.error("❌ Failed to extract text from resume.")
 
-
 def application_tracking_dashboard():
-    st.header("Application Tracking Dashboard")
+    st.header("📋 Application Tracking Dashboard")
 
     # Initialize database
     init_db()
 
     # Form to add a new application
-    st.subheader("Add New Application")
+    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")
+        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:
             if uploaded_file:
                 job_description = extract_text_from_pdf(uploaded_file)
@@ -732,10 +1265,10 @@ def application_tracking_dashboard():
                 resume_text=resume_text,
                 skills=skills
             )
-            st.success("Application added successfully!")
+            st.success("✅ Application added successfully!")
 
     # Display applications
-    st.subheader("Your Applications")
+    st.subheader("📊 Your Applications")
     applications = fetch_applications()
     if applications:
         df = pd.DataFrame(applications)
@@ -745,22 +1278,22 @@ def application_tracking_dashboard():
         # Export Button
         csv = df.to_csv(index=False).encode('utf-8')
         st.download_button(
-            label="Download Applications as CSV",
+            label="💾 Download Applications as CSV",
             data=csv,
             file_name='applications.csv',
             mime='text/csv',
         )
 
         # Import Button
-        st.subheader("Import Applications")
-        uploaded_csv = st.file_uploader("Upload a CSV file", type="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)
                 # Validate required columns
                 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.")
+                    st.error("❌ Uploaded CSV is missing required columns.")
                 else:
                     for index, row in imported_df.iterrows():
                         job_title = row.get("Job Title", "N/A")
@@ -783,36 +1316,99 @@ def application_tracking_dashboard():
                             resume_text=resume_text,
                             skills=skills
                         )
-                    st.success("Applications imported successfully!")
+                    st.success("✅ Applications imported successfully!")
             except Exception as e:
-                st.error(f"Error importing applications: {e}")
+                st.error(f"❌ Error importing applications: {e}")
 
         # Actions: Update Status or Delete
         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']}")
+                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']}")
                 if app['Job Description']:
-                    st.write("**Job Description:**")
+                    st.write("**📄 Job Description:**")
                     st.write(app['Job Description'][:500] + "...")
                 if app['Skills']:
-                    st.write("**Skills:**", ', '.join(app['Skills']))
+                    st.write("**💼 Skills:**", ', '.join(app['Skills']))
                 # Update status
-                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']}"):
+                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!")
+                    st.success("✅ Status updated successfully!")
                 # Delete application
-                if st.button("Delete Application", key=f"delete_{app['ID']}"):
+                if st.button("🗑️ Delete Application", key=f"delete_{app['ID']}"):
                     delete_application(app['ID'])
-                    st.success("Application deleted successfully!")
+                    st.success("✅ Application deleted successfully!")
     else:
-        st.write("No applications found.")
+        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. Get personalized recommendations from multiple job platforms.
+    """)
+
+    # User Preferences Form
+    st.subheader("🎯 Set Your Preferences")
+    with st.form("preferences_form"):
+        job_title = st.text_input("🔍 Desired Job Title", placeholder="e.g., Data Scientist, Backend Developer")
+        location = st.text_input("📍 Preferred Location", placeholder="e.g., New York, NY, 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..."):
+                # Fetch recommendations from Remotive
+                remotive_jobs = recommend_jobs(user_skills, user_preferences)
+                # Fetch recommendations from The Muse
+                muse_jobs = recommend_muse_jobs(user_skills, user_preferences)
+                # Fetch recommendations from Adzuna
+                adzuna_jobs = recommend_adzuna_jobs(user_skills, user_preferences)
+
+                # Combine all job listings
+                combined_jobs = remotive_jobs + muse_jobs + adzuna_jobs
+                # Remove duplicates based on job URL
+                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
+
+                if unique_jobs:
+                    st.subheader("💼 Recommended Jobs:")
+                    for idx, job in enumerate(unique_jobs.values(), 1):
+                        job_title_display = job.get("title") or job.get("name")
+                        company_display = job.get("company", {}).get("name") or job.get("company_name")
+                        location_display = job.get("candidate_required_location") or job.get("location")
+                        salary_display = f"${job.get('salary_min', 'N/A')} - ${job.get('salary_max', 'N/A')}" if job.get('salary_min') or job.get('salary_max') else "N/A"
+                        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"**💰 Salary:** {salary_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.header("🎤 Interview Preparation")
 
     st.write("""
     Prepare for your interviews with tailored mock questions and expert answers.
@@ -821,15 +1417,15 @@ def interview_preparation_module():
     # Create two columns for input fields
     col1, col2 = st.columns(2)
     with col1:
-        job_title = st.text_input("Enter the job title you're applying for:")
+        job_title = st.text_input("🔍 Enter the job title you're applying for:")
     with col2:
-        company = st.text_input("Enter the company name:")
+        company = st.text_input("🏢 Enter the company name:")
 
-    if st.button("Generate Mock Interview Questions"):
+    if st.button("🎯 Generate Mock Interview Questions"):
         if not job_title or not company:
-            st.error("Please enter both job title and company name.")
+            st.error("❌ Please enter both job title and company name.")
             return
-        with st.spinner("Generating questions..."):
+        with st.spinner("⏳ Generating questions..."):
             # Prompt to generate 50 interview questions with answers
             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.
@@ -840,7 +1436,7 @@ def interview_preparation_module():
                 qa_text = llm.invoke(prompt).content.strip()
                 # Split into question-answer pairs
                 qa_pairs = qa_text.split('\n\n')
-                st.subheader("Mock Interview Questions and Answers:")
+                st.subheader("🗣️ Mock Interview Questions and Answers:")
                 for idx, qa in enumerate(qa_pairs, 1):
                     if qa.strip():
                         parts = qa.split('\n', 1)
@@ -851,10 +1447,10 @@ def interview_preparation_module():
                             st.markdown(f"**A:** {answer}")
                             st.write("---")
             except Exception as e:
-                st.error(f"Error generating interview questions: {e}")
+                st.error(f"❌ Error generating interview questions: {e}")
 
 def personalized_learning_paths_module():
-    st.header("Personalized Learning Paths")
+    st.header("📚 Personalized Learning Paths")
 
     st.write("""
     Receive tailored learning plans to help you acquire the skills needed for your desired career, complemented with curated video resources.
@@ -863,18 +1459,18 @@ def personalized_learning_paths_module():
     # Create two columns for input fields
     col1, col2 = st.columns(2)
     with col1:
-        career_goal = st.text_input("Enter your career goal (e.g., Data Scientist, Machine Learning Engineer):")
+        career_goal = st.text_input("🎯 Enter your career goal (e.g., Data Scientist, Machine Learning Engineer):")
     with col2:
-        current_skills = st.text_input("Enter your current skills (comma-separated):")
+        current_skills = st.text_input("💡 Enter your current skills (comma-separated):")
 
-    if st.button("Generate Learning Path"):
+    if st.button("🚀 Generate Learning Path"):
         if not career_goal or not current_skills:
-            st.error("Please enter both career goal and current skills.")
+            st.error("❌ Please enter both career goal and current skills.")
             return
-        with st.spinner("Generating your personalized learning path..."):
+        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.subheader("📜 Your Personalized Learning Path:")
                 st.write(learning_path)
                 
                 # Assuming the learning path is divided into modules/subparts separated by newlines or numbering
@@ -888,7 +1484,7 @@ def personalized_learning_paths_module():
                 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:")
+                st.subheader("📹 Recommended YouTube Videos for Each Module:")
                 for module in modules:
                     # Search for long videos related to the module
                     video_urls = search_youtube_videos(query=module, max_results=2, video_duration="long")
@@ -898,10 +1494,10 @@ def personalized_learning_paths_module():
                     else:
                         st.write(f"No videos found for **{module}**.")
             else:
-                st.error("Failed to generate learning path.")
+                st.error("❌ Failed to generate learning path.")
 
 def networking_opportunities_module():
-    st.header("Networking Opportunities")
+    st.header("🤝 Networking Opportunities")
 
     st.write("""
     Expand your professional network by connecting with relevant industry peers and joining professional groups.
@@ -910,28 +1506,28 @@ def networking_opportunities_module():
     # Create two columns for input fields
     col1, col2 = st.columns(2)
     with col1:
-        user_skills = st.text_input("Enter your key skills (comma-separated):")
+        user_skills = st.text_input("💡 Enter your key skills (comma-separated):")
     with col2:
-        industry = st.text_input("Enter your industry (e.g., Technology, Finance):")
+        industry = st.text_input("🏭 Enter your industry (e.g., Technology, Finance):")
 
-    if st.button("Find Networking Opportunities"):
+    if st.button("🔍 Find Networking Opportunities"):
         if not user_skills or not industry:
-            st.error("Please enter both key skills and industry.")
+            st.error("❌ Please enter both key skills and industry.")
             return
-        with st.spinner("Fetching networking opportunities..."):
+        with st.spinner("🔄 Fetching networking opportunities..."):
             # Suggest LinkedIn groups or connections based on skills and industry
             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.subheader("🔗 Recommended Networking Groups and Events:")
                 st.write(suggestions)
             except Exception as e:
-                st.error(f"Error fetching networking opportunities: {e}")
+                st.error(f"❌ Error fetching networking opportunities: {e}")
 
 def salary_estimation_module():
-    st.header("Salary Estimation and Negotiation Tips")
+    st.header("💵 Salary Estimation and Negotiation Tips")
 
     st.write("""
     Understand the salary expectations for your desired roles and learn effective negotiation strategies.
@@ -940,15 +1536,15 @@ def salary_estimation_module():
     # Create two columns for input fields
     col1, col2 = st.columns(2)
     with col1:
-        job_title = st.text_input("Enter the job title:")
+        job_title = st.text_input("🔍 Enter the job title:")
     with col2:
-        location = st.text_input("Enter the location (e.g., New York, NY, USA):")
+        location = st.text_input("📍 Enter the location (e.g., New York, NY, USA):")
 
-    if st.button("Get Salary Estimate"):
+    if st.button("💰 Get Salary Estimate"):
         if not job_title or not location:
-            st.error("Please enter both job title and location.")
+            st.error("❌ Please enter both job title and location.")
             return
-        with st.spinner("Fetching salary data..."):
+        with st.spinner("🔄 Fetching salary data..."):
             # Job Salary Data API Integration
             salary_data = get_job_recommendations(job_title, location)
             if salary_data:
@@ -957,7 +1553,7 @@ def salary_estimation_module():
                 max_salary = salary_data.get("max_salary")
 
                 if min_salary and avg_salary and max_salary:
-                    st.subheader("Salary Estimate:")
+                    st.subheader("💲 Salary Estimate:")
                     st.write(f"**Minimum Salary:** ${min_salary:,}")
                     st.write(f"**Average Salary:** ${avg_salary:,}")
                     st.write(f"**Maximum Salary:** ${max_salary:,}")
@@ -972,9 +1568,9 @@ def salary_estimation_module():
                                  title=f"Salary Estimates for {job_title} in {location}",
                                  labels={"Amount": "Salary (USD)"},
                                  text_auto=True)
-                    st.plotly_chart(fig)
+                    st.plotly_chart(fig, use_container_width=True)
                 else:
-                    st.error("Salary data not available for the provided job title and location.")
+                    st.error("❌ Salary data not available for the provided job title and location.")
 
                 # Generate negotiation tips using Groq
                 tips_prompt = f"""
@@ -982,38 +1578,38 @@ def salary_estimation_module():
                 """
                 try:
                     tips = llm.invoke(tips_prompt).content.strip()
-                    st.subheader("Negotiation Tips:")
+                    st.subheader("📝 Negotiation Tips:")
                     st.write(tips)
                 except Exception as e:
-                    st.error(f"Error generating negotiation tips: {e}")
+                    st.error(f"❌ Error generating negotiation tips: {e}")
             else:
-                st.error("Failed to retrieve salary data.")
+                st.error("❌ Failed to retrieve salary data.")
 
 def feedback_and_improvement_module():
-    st.header("Feedback and Continuous Improvement")
+    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")
-
+        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.")
+                st.error("❌ Please fill in all the fields.")
             else:
                 # Here you can implement logic to store feedback, e.g., in a database or send via email
                 # For demonstration, we'll print to the console
                 print(f"Feedback from {name} ({email}): {feedback_type} - {feedback}")
-                st.success("Thank you for your feedback!")
+                st.success("✅ Thank you for your feedback!")
 
 def gamification_module():
-    st.header("Gamification and Achievements")
+    st.header("🏆 Gamification and Achievements")
 
     st.write("""
     Stay motivated by earning badges and tracking your progress!
@@ -1046,7 +1642,7 @@ def gamification_module():
     st.write(f"{progress * 100:.0f}% complete")
 
 def resource_library_page():
-    st.header("Resource Library")
+    st.header("📚 Resource Library")
 
     st.write("""
     Access a collection of templates and guides to enhance your job search.
@@ -1076,17 +1672,17 @@ def resource_library_page():
         try:
             with open(resource['file'], "rb") as file:
                 btn = st.download_button(
-                    label="Download",
+                    label="⬇️ 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.error(f"❌ File {resource['file']} not found. Please ensure the file is in the correct directory.")
         st.write("---")
 
 def success_stories_page():
-    st.header("Success Stories")
+    st.header("🌟 Success Stories")
 
     st.write("""
     Hear from our users who have successfully landed their dream jobs with our assistance!
@@ -1121,36 +1717,8 @@ def success_stories_page():
             st.write(f"\"{user['testimonial']}\"")
             st.write("---")
 
-def help_page():
-    st.header("Help & FAQ")
-
-    with st.expander("How do I generate a cover letter?"):
-        st.write("""
-            To generate a cover letter, navigate to the **Cover Letter Generator** section, enter the job link, upload your resume, and click on **Generate Cover Letter**.
-        """)
-
-    with st.expander("How do I track my applications?"):
-        st.write("""
-            Use the **Application Tracking** dashboard to add new applications, update their status, and monitor deadlines.
-        """)
-
-    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 containing your applications. Ensure the CSV has the required columns.
-        """)
-
-    with st.expander("How do I provide feedback?"):
-        st.write("""
-            Navigate to the **Feedback and Continuous Improvement** section, fill out the form, and submit your feedback.
-        """)
-
 def chatbot_support_page():
-    st.header("AI-Powered Chatbot Support")
+    st.header("🤖 AI-Powered Chatbot Support")
 
     st.write("""
     Have questions or need assistance? Chat with our AI-powered assistant!
@@ -1161,7 +1729,7 @@ def chatbot_support_page():
         st.session_state['chat_history'] = []
 
     # User input
-    user_input = st.text_input("You:", key="user_input")
+    user_input = st.text_input("🗨️ You:", key="user_input")
 
     if st.button("Send"):
         if user_input:
@@ -1174,13 +1742,14 @@ def chatbot_support_page():
             """
             try:
                 # Invoke the LLM to get a response
-                response = llm.invoke(prompt).content.strip()
+                response = llm.invoke(prompt)
+                assistant_message = response.content.strip()
                 # Append assistant response to chat history
-                st.session_state['chat_history'].append({"message": response, "is_user": False})
+                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."
+                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}")
+                st.error(f"❌ Error in chatbot: {e}")
 
     # Display chat history using streamlit-chat
     for chat in st.session_state['chat_history']:
@@ -1189,6 +1758,175 @@ def chatbot_support_page():
         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("""
+            To generate a cover letter, navigate to the **Cover Letter Generator** section, enter the job link, upload your resume, and click on **Generate Cover Letter**.
+        """)
+
+    with st.expander("📋 How do I track my applications?"):
+        st.write("""
+            Use the **Application Tracking Dashboard** to add new applications, update their status, and monitor deadlines.
+        """)
+
+    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 containing your applications. Ensure the CSV has the required columns.
+        """)
+
+    with st.expander("🗣️ How do I provide feedback?"):
+        st.write("""
+            Navigate to the **Feedback and Continuous Improvement** section, fill out the form, and submit your feedback.
+        """)
+
+# -------------------------------
+# YouTube Video Search and Embed Functions
+# -------------------------------
+
+@st.cache_data(ttl=86400)  # Cache results for 1 day
+def search_youtube_videos(query, max_results=2, video_duration="long"):
+    """
+    Searches YouTube for videos matching the query and returns video URLs.
+    
+    Args:
+        query (str): Search query.
+        max_results (int, optional): Number of videos to return. Defaults to 2.
+        video_duration (str, optional): Duration filter ('any', 'short', 'medium', 'long'). Defaults to "long".
+    
+    Returns:
+        list: List of YouTube video URLs.
+    """
+    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 in the Streamlit app.
+    
+    Args:
+        video_urls (list): List of YouTube video URLs.
+        module_name (str): Name of the module for context.
+    """
+    for url in video_urls:
+        st.video(url)
+
+# -------------------------------
+# Job Recommendations and Labor Market Insights
+# -------------------------------
+
+def job_recommendations_module():
+    st.header("🔍 Job Matching & Recommendations")
+
+    st.write("""
+    Discover job opportunities tailored to your skills and preferences. Get personalized recommendations from multiple job platforms.
+    """)
+
+    # User Preferences Form
+    st.subheader("🎯 Set Your Preferences")
+    with st.form("preferences_form"):
+        job_title = st.text_input("🔍 Desired Job Title", placeholder="e.g., Data Scientist, Backend Developer")
+        location = st.text_input("📍 Preferred Location", placeholder="e.g., New York, NY, 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..."):
+                # Fetch recommendations from Remotive
+                remotive_jobs = recommend_jobs(user_skills, user_preferences)
+                # Fetch recommendations from The Muse
+                muse_jobs = recommend_muse_jobs(user_skills, user_preferences)
+                # Fetch recommendations from Adzuna
+                adzuna_jobs = recommend_adzuna_jobs(user_skills, user_preferences)
+
+                # Combine all job listings
+                combined_jobs = remotive_jobs + muse_jobs + adzuna_jobs
+                # Remove duplicates based on job URL
+                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
+
+                if unique_jobs:
+                    st.subheader("💼 Recommended Jobs:")
+                    for idx, job in enumerate(unique_jobs.values(), 1):
+                        job_title_display = job.get("title") or job.get("name")
+                        company_display = job.get("company", {}).get("name") or job.get("company_name")
+                        location_display = job.get("candidate_required_location") or job.get("location")
+                        salary_display = f"${job.get('salary_min', 'N/A')} - ${job.get('salary_max', 'N/A')}" if job.get('salary_min') or job.get('salary_max') else "N/A"
+                        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"**💰 Salary:** {salary_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 labor_market_insights_module():
+    st.header("📈 Labor Market Insights")
+
+    st.write("""
+    Gain valuable insights into the current labor market trends, employment rates, and industry growth to make informed career decisions.
+    """)
+
+    # Define BLS Series IDs based on desired data
+    # Example: Unemployment rate (Series ID: LNS14000000)
+    # Reference: https://www.bls.gov/web/laus/laumstrk.htm
+    unemployment_series_id = "LNS14000000"  # Unemployment Rate
+    employment_series_id = "CEU0000000001"  # Total Employment
+
+    # Display Unemployment Rate
+    display_bls_data(unemployment_series_id, "Unemployment Rate (%)")
+
+    # Display Total Employment
+    display_bls_data(employment_series_id, "Total Employment")
+
+    # Additional Insights
+    st.subheader("💡 Additional Insights")
+    st.write("""
+    - **Industry Growth:** Understanding which industries are growing can help you target your job search effectively.
+    - **Salary Trends:** Keeping an eye on salary trends ensures that you negotiate effectively and align your expectations.
+    - **Geographical Demand:** Some regions may have higher demand for certain roles, guiding your location preferences.
+    """)
+
 # -------------------------------
 # Main App Function
 # -------------------------------
@@ -1213,12 +1951,15 @@ def main_app():
     # Sidebar Navigation using streamlit_option_menu
     with st.sidebar:
         selected = option_menu(
-            menu_title="Main Menu",
+            menu_title="📂 Main Menu",
             options=["Email Generator", "Cover Letter Generator", "Resume Analysis", "Application Tracking",
-                     "Interview Preparation", "Personalized Learning Paths", "Networking Opportunities",
-                     "Salary Estimation", "Feedback", "Gamification", "Resource Library", "Success Stories", "Chatbot Support", "Help"],
-            icons=["envelope", "file-earmark-text", "file-person", "briefcase", "gear",
-                   "book", "people", "currency-dollar", "chat-left-text", "trophy", "collection", "star", "chat", "question-circle"],
+                     "Job Recommendations", "Labor Market Insights", "Interview Preparation", "Personalized Learning Paths",
+                     "Networking Opportunities", "Salary Estimation", "Feedback", "Gamification", "Resource Library",
+                     "Success Stories", "Chatbot Support", "Help"],
+            icons=["envelope", "file-earmark-text", "file-person", "briefcase",
+                   "search", "bar-chart-line", "microphone", "book",
+                   "people", "currency-dollar", "chat-left-text", "trophy", "collection",
+                   "star", "robot", "question-circle"],
             menu_icon="cast",
             default_index=0,
             styles={
@@ -1238,6 +1979,10 @@ def main_app():
         resume_analysis_page()
     elif selected == "Application Tracking":
         application_tracking_dashboard()
+    elif selected == "Job Recommendations":
+        job_recommendations_module()
+    elif selected == "Labor Market Insights":
+        labor_market_insights_module()
     elif selected == "Interview Preparation":
         interview_preparation_module()
     elif selected == "Personalized Learning Paths":