Update app.py

app.py

@@ -1,5 +1,3 @@
-# app.py
-
 import streamlit as st
 import requests
 from langchain_groq import ChatGroq
@@ -14,6 +12,7 @@ from streamlit_option_menu import option_menu
 import fitz  # PyMuPDF
 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"]
@@ -203,49 +202,6 @@ def suggest_keywords(resume_text, job_description=None):
         st.error(f"Error suggesting keywords: {e}")
         return []
 
-@st.cache_data(ttl=3600)
-def get_job_recommendations(job_title, location="India"):
-    """
-    Fetches salary estimates using the Job Salary Data API based on the job title and location.
-    """
-    url = "https://job-salary-data.p.rapidapi.com/job-salary"
-    querystring = {
-        "job_title": job_title.strip(),
-        "location": location.strip(),
-        "radius": "100"  # Adjust radius as needed
-    }
-
-    headers = {
-        "x-rapidapi-key": RAPIDAPI_KEY,  # Securely access the API key
-        "x-rapidapi-host": "job-salary-data.p.rapidapi.com"
-    }
-
-    try:
-        response = requests.get(url, headers=headers, params=querystring)
-        response.raise_for_status()
-        salary_data = response.json()
-
-        # Adjust the keys based on the API's response structure
-        min_salary = salary_data.get("min_salary")
-        avg_salary = salary_data.get("avg_salary")
-        max_salary = salary_data.get("max_salary")
-
-        if not all([min_salary, avg_salary, max_salary]):
-            st.error("Incomplete salary data received from the API.")
-            return {}
-
-        return {
-            "min_salary": min_salary,
-            "avg_salary": avg_salary,
-            "max_salary": max_salary
-        }
-    except requests.exceptions.HTTPError as http_err:
-        st.error(f"HTTP error occurred: {http_err}")
-        return {}
-    except Exception as err:
-        st.error(f"An error occurred: {err}")
-        return {}
-
 def create_skill_distribution_chart(skills):
     """
     Creates a bar chart showing the distribution of skills.
@@ -394,94 +350,110 @@ def fetch_muse_jobs_api(job_title, location=None, category=None, max_results=50)
 
 # Adzuna API Integration
 @st.cache_data(ttl=86400)  # Cache results for 1 day
-def fetch_adzuna_jobs_api(job_title, location="us", category=None, max_results=50):
+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 (e.g., "Front end developer").
-        location (str, optional): The country code for the job location (e.g., "us" for USA). Defaults to "us".
+        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.
     """
-    # Mapping common location inputs to Adzuna country codes
-    location_mapping = {
-        "usa": "us",
-        "united states": "us",
-        "us": "us",
-        "india": "in",
-        "uk": "gb",
-        "united kingdom": "gb",
-        "canada": "ca",
-        # Add more mappings as needed
-    }
-    
-    # Normalize and map the location input
-    normalized_location = location.strip().lower()
-    country_code = location_mapping.get(normalized_location, normalized_location)
-    
-    # Construct the API endpoint
-    base_url = f"https://api.adzuna.com/v1/api/jobs/{country_code}/search/1"
-    
-    # Prepare query parameters
+    base_url = f"https://api.adzuna.com/v1/api/jobs/{location}/search/1"
     params = {
-        "app_id": st.secrets["ADZUNA_APP_ID"],    # Ensure ADZUNA_APP_ID is correctly set in secrets.toml
-        "app_key": st.secrets["ADZUNA_APP_KEY"],  # Ensure ADZUNA_APP_KEY is correctly set in secrets.toml
-        "what": job_title.strip(),
+        "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()  # Raises HTTPError for bad responses (4xx or 5xx)
+        response.raise_for_status()
         jobs = response.json().get("results", [])
-        
-        if not jobs:
-            st.info("ℹ️ No job listings found for the specified criteria.")
-        
         return jobs
-    
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error fetching jobs from Adzuna: {e}")
+        return []
+
+# Indeed API Integration
+@st.cache_data(ttl=86400)  # Cache results for 1 day
+def fetch_indeed_jobs_api(job_title, country="CA", sort="-1", page_size=50):
+    """
+    Fetches job listings from Indeed API based on user preferences.
+
+    Args:
+        job_title (str): The job title to search for (e.g., "Front end developer").
+        country (str, optional): The country code (e.g., "CA" for Canada). Defaults to "CA".
+        sort (str, optional): Sorting parameter (e.g., "-1" for relevance). Defaults to "-1".
+        page_size (int, optional): Number of results per page. Defaults to 50.
+
+    Returns:
+        list: A list of job dictionaries.
+    """
+    url = "https://indeed46.p.rapidapi.com/job"
+
+    # Encode job title to handle spaces and special characters
+    encoded_job_title = re.sub(r'\s+', '+', job_title.strip())
+
+    querystring = {
+        "country": country,
+        "sort": sort,
+        "page_size": str(page_size),
+        "title": encoded_job_title  # Assuming the API accepts a 'title' parameter for job titles
+    }
+
+    headers = {
+        "x-rapidapi-key": RAPIDAPI_KEY,  # Ensure RAPIDAPI_KEY is set in .streamlit/secrets.toml
+        "x-rapidapi-host": "indeed46.p.rapidapi.com"
+    }
+
+    try:
+        response = requests.get(url, headers=headers, params=querystring)
+        response.raise_for_status()  # Raises HTTPError for bad responses (4xx or 5xx)
+        data = response.json()
+        jobs = data.get("data", [])
+        return jobs
     except requests.exceptions.HTTPError as http_err:
-        if response.status_code == 403:
-            st.error("❌ Access Forbidden: Check your API credentials and permissions.")
+        if response.status_code == 400:
+            st.error("❌ Bad Request: Please check the parameters you're sending.")
+        elif response.status_code == 403:
+            st.error("❌ Access Forbidden: Check your API key and permissions.")
         elif response.status_code == 404:
             st.error("❌ Resource Not Found: Verify the endpoint and parameters.")
         else:
             st.error(f"❌ HTTP error occurred: {http_err}")
         return []
-    
     except requests.exceptions.RequestException as req_err:
         st.error(f"❌ Request Exception: {req_err}")
         return []
-    
     except Exception as e:
         st.error(f"❌ An unexpected error occurred: {e}")
         return []
-        
-def recommend_jobs(user_skills, user_preferences):
+
+def recommend_indeed_jobs(user_skills, user_preferences):
     """
-    Recommends jobs based on user skills and preferences from Remotive API.
-    
+    Recommends jobs from Indeed 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")
+    country = user_preferences.get("location_country", "CA")
     category = user_preferences.get("category")
-    
-    jobs = fetch_remotive_jobs_api(job_title, location, category)
-    
+
+    jobs = fetch_indeed_jobs_api(job_title, country, sort="-1", page_size=50)
+
     # Simple matching based on skills appearing in job description
     recommended_jobs = []
     for job in jobs:
@@ -489,74 +461,47 @@ def recommend_jobs(user_skills, user_preferences):
         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):
+def recommend_jobs(user_skills, user_preferences):
     """
-    Recommends jobs from Adzuna API based on user skills and preferences.
-    
+    Recommends jobs based on user skills and preferences from Remotive, The Muse, Adzuna, and Indeed APIs.
+
     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
+    # Fetch from Remotive
+    remotive_jobs = fetch_remotive_jobs_api(user_preferences.get("job_title", ""), user_preferences.get("location"), user_preferences.get("category"))
+
+    # Fetch from The Muse
+    muse_jobs = fetch_muse_jobs_api(user_preferences.get("job_title", ""), user_preferences.get("location"), user_preferences.get("category"))
+
+    # Fetch from Adzuna
+    adzuna_jobs = fetch_adzuna_jobs_api(user_preferences.get("job_title", ""), user_preferences.get("location", "india"), user_preferences.get("category"))
+
+    # Fetch from Indeed
+    indeed_jobs = recommend_indeed_jobs(user_skills, user_preferences)
+
+    # Combine all job listings
+    combined_jobs = remotive_jobs + muse_jobs + adzuna_jobs + indeed_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
+
+    return list(unique_jobs.values())
 
 # -------------------------------
 # BLS API Integration and Display
@@ -566,12 +511,12 @@ def recommend_adzuna_jobs(user_skills, user_preferences):
 def fetch_bls_data(series_ids, start_year=2020, end_year=datetime.now().year):
     """
     Fetches labor market data from the BLS API.
-    
+
     Args:
         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.
     """
@@ -600,7 +545,7 @@ def fetch_bls_data(series_ids, start_year=2020, end_year=datetime.now().year):
 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.
@@ -609,20 +554,20 @@ def display_bls_data(series_id, title):
     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)
@@ -728,298 +673,68 @@ def generate_learning_path(career_goal, current_skills):
     {career_goal}
 
     **Current Skills:**
-    {current_skills}
-
-    **Learning Path:**
-    """
-
-    try:
-        response = llm.invoke(prompt)
-        learning_path = response.content.strip()
-        return learning_path
-    except Exception as e:
-        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
+    {current_skills}
+
+    **Learning Path:**
+    """
+
+    try:
+        response = llm.invoke(prompt)
+        learning_path = response.content.strip()
+        return learning_path
+    except Exception as e:
+        st.error(f"Error generating learning path: {e}")
+        return ""
+
+# -------------------------------
+# YouTube Video Search and Embed Functions
+# -------------------------------
 
-def recommend_muse_jobs(user_skills, user_preferences):
+@st.cache_data(ttl=86400)  # Cache results for 1 day
+def search_youtube_videos(query, max_results=2, video_duration="long"):
     """
-    Recommends jobs from The Muse API based on user skills and preferences.
+    Searches YouTube for videos matching the query and returns video URLs.
     
     Args:
-        user_skills (list): List of user's skills.
-        user_preferences (dict): User preferences like job title, location, category.
+        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: Recommended job listings.
+        list: List of YouTube video URLs.
     """
-    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
+    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 recommend_adzuna_jobs(user_skills, user_preferences):
+def embed_youtube_videos(video_urls, module_name):
     """
-    Recommends jobs from Adzuna API based on user skills and preferences.
+    Embeds YouTube videos in the Streamlit app.
     
     Args:
-        user_skills (list): List of user's skills.
-        user_preferences (dict): User preferences like job title, location, category.
-    
-    Returns:
-        list: Recommended job listings.
+        video_urls (list): List of YouTube video URLs.
+        module_name (str): Name of the module for context.
     """
-    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
+    for url in video_urls:
+        st.video(url)
 
 # -------------------------------
-# Labor Market Insights Module
+# Job Recommendations and BLS Integration
 # -------------------------------
 
 def labor_market_insights_module():
@@ -1049,8 +764,6 @@ def labor_market_insights_module():
     - **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
 # -------------------------------
@@ -1403,7 +1116,7 @@ def job_recommendations_module():
             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,
@@ -1412,35 +1125,22 @@ def job_recommendations_module():
             }
 
             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:
+                # Fetch recommendations from all APIs
+                recommended_jobs = recommend_jobs(user_skills, user_preferences)
+
+                if recommended_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"
+                    for idx, job in enumerate(recommended_jobs, 1):
+                        # Depending on the API, job data structure might differ
+                        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")
+                        salary_display = "N/A"  # Salary is removed
                         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:
@@ -1565,65 +1265,6 @@ def networking_opportunities_module():
             except Exception as e:
                 st.error(f"❌ Error fetching networking opportunities: {e}")
 
-def salary_estimation_module():
-    st.header("💵 Salary Estimation and Negotiation Tips")
-
-    st.write("""
-    Understand the salary expectations for your desired roles and learn effective negotiation strategies.
-    """)
-
-    # Create two columns for input fields
-    col1, col2 = st.columns(2)
-    with col1:
-        job_title = st.text_input("🔍 Enter the job title:")
-    with col2:
-        location = st.text_input("📍 Enter the location (e.g., New York, NY, USA):")
-
-    if st.button("💰 Get Salary Estimate"):
-        if not job_title or not location:
-            st.error("❌ Please enter both job title and location.")
-            return
-        with st.spinner("🔄 Fetching salary data..."):
-            # Job Salary Data API Integration
-            salary_data = get_job_recommendations(job_title, location)
-            if salary_data:
-                min_salary = salary_data.get("min_salary")
-                avg_salary = salary_data.get("avg_salary")
-                max_salary = salary_data.get("max_salary")
-
-                if min_salary and avg_salary and max_salary:
-                    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:,}")
-
-                    # Visualization
-                    salary_df = pd.DataFrame({
-                        "Salary Range": ["Minimum", "Average", "Maximum"],
-                        "Amount": [min_salary, avg_salary, max_salary]
-                    })
-
-                    fig = px.bar(salary_df, x="Salary Range", y="Amount",
-                                 title=f"Salary Estimates for {job_title} in {location}",
-                                 labels={"Amount": "Salary (USD)"},
-                                 text_auto=True)
-                    st.plotly_chart(fig, use_container_width=True)
-                else:
-                    st.error("❌ Salary data not available for the provided job title and location.")
-
-                # Generate negotiation tips using Groq
-                tips_prompt = f"""
-                Provide a list of 5 effective tips for negotiating a salary for a {job_title} position in {location}.
-                """
-                try:
-                    tips = llm.invoke(tips_prompt).content.strip()
-                    st.subheader("📝 Negotiation Tips:")
-                    st.write(tips)
-                except Exception as e:
-                    st.error(f"❌ Error generating negotiation tips: {e}")
-            else:
-                st.error("❌ Failed to retrieve salary data.")
-
 def feedback_and_improvement_module():
     st.header("🗣️ Feedback and Continuous Improvement")
 
@@ -1825,209 +1466,6 @@ def help_page():
             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)
-
-def fetch_jsearch_estimated_salary(job_title, location, radius=100):
-    """
-    Fetches estimated salary data for a given job title and location using the JSearch API.
-    
-    Args:
-        job_title (str): The job title to search for (e.g., "NodeJS Developer").
-        location (str): The job location (e.g., "New York, NY, USA").
-        radius (int, optional): Search radius in miles. Defaults to 100.
-    
-    Returns:
-        dict: A dictionary containing minimum, average, and maximum salary estimates.
-    """
-    url = "https://jsearch.p.rapidapi.com/estimated-salary"
-    
-    # Encode job title and location to handle spaces and special characters
-    encoded_job_title = quote(job_title.strip())
-    encoded_location = quote(location.strip())
-    
-    querystring = {
-        "job_title": encoded_job_title,
-        "location": encoded_location,
-        "radius": str(radius)
-    }
-    
-    headers = {
-        "x-rapidapi-key": st.secrets["RAPIDAPI_KEY"],  # Ensure RAPIDAPI_KEY is correctly set in secrets.toml
-        "x-rapidapi-host": "jsearch.p.rapidapi.com"
-    }
-    
-    try:
-        response = requests.get(url, headers=headers, params=querystring)
-        response.raise_for_status()  # Raises HTTPError for bad responses (4xx or 5xx)
-        data = response.json()
-        
-        # Extract salary estimates
-        min_salary = data.get("min_salary")
-        avg_salary = data.get("avg_salary")
-        max_salary = data.get("max_salary")
-        
-        if not all([min_salary, avg_salary, max_salary]):
-            st.error("❌ Incomplete salary data received from the API.")
-            return {}
-        
-        return {
-            "min_salary": min_salary,
-            "avg_salary": avg_salary,
-            "max_salary": max_salary
-        }
-    
-    except requests.exceptions.HTTPError as http_err:
-        if response.status_code == 403:
-            st.error("❌ Access Forbidden: Check your API key and permissions.")
-        elif response.status_code == 404:
-            st.error("❌ Resource Not Found: Verify the endpoint and parameters.")
-        else:
-            st.error(f"❌ HTTP error occurred: {http_err}")
-        return {}
-    
-    except requests.exceptions.RequestException as req_err:
-        st.error(f"❌ Request Exception: {req_err}")
-        return {}
-    
-    except Exception as e:
-        st.error(f"❌ An unexpected error occurred: {e}")
-        return {}
-
-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
 # -------------------------------
@@ -2055,11 +1493,11 @@ def main_app():
             menu_title="📂 Main Menu",
             options=["Email Generator", "Cover Letter Generator", "Resume Analysis", "Application Tracking",
                      "Job Recommendations", "Labor Market Insights", "Interview Preparation", "Personalized Learning Paths",
-                     "Networking Opportunities", "Salary Estimation", "Feedback", "Gamification", "Resource Library",
+                     "Networking Opportunities", "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",
+                   "people", "chat-left-text", "trophy", "collection",
                    "star", "robot", "question-circle"],
             menu_icon="cast",
             default_index=0,
@@ -2090,8 +1528,6 @@ def main_app():
         personalized_learning_paths_module()
     elif selected == "Networking Opportunities":
         networking_opportunities_module()
-    elif selected == "Salary Estimation":
-        salary_estimation_module()
     elif selected == "Feedback":
         feedback_and_improvement_module()
     elif selected == "Gamification":
@@ -2105,6 +1541,5 @@ def main_app():
     elif selected == "Help":
         help_page()
 
-
 if __name__ == "__main__":
     main_app()