Update app.py

app.py

@@ -189,15 +189,68 @@ def display_dashboard(df):
     with col4:
         fig = create_chart(top_job_titles, top_job_titles.index, top_job_titles.values, "Top 20 Job Titles", ['#59a14f'])
         st.plotly_chart(fig, use_container_width=True)
+def display_data_explorer(df):
+    st.subheader("Data Explorer")
+
+    show_all = st.radio("Display", ("All Data", "Filtered Data"))
+
+    if show_all == "Filtered Data":
+        unique_values = get_unique_values(df)
+        col1, col2, col3, col4,col5 = st.columns(5)
+        with col1:
+            companies = st.multiselect("Select Companies", options=unique_values['companies'])
+        with col2:
+            locations = st.multiselect("Select Locations", options=unique_values['locations'])
+        with col3:
+            job_types = st.multiselect("Select Job Types", options=unique_values['job_types'])
+        with col4:
+            Role_Name = st.multiselect("Select Role Types", options=unique_values['Role_Name'])
+        with col5:
+            Date_posted = st.multiselect("Select Date Posted", options=unique_values['Date_posted'])
+        
+        filtered_df = filter_dataframe(df, companies, locations, job_types, Role_Name,Date_posted)
+    else:
+        filtered_df = df
+
+    st.write(f"Showing {len(filtered_df)} job listings")
+    
+    # Pagination
+    items_per_page = 15
+    num_pages = math.ceil(len(filtered_df) / items_per_page)
+    
+    col1, col2, col3 = st.columns([1, 3, 1])
+    with col2:
+        page = st.number_input("Page", min_value=1, max_value=num_pages, value=1)
+    
+    start_idx = (page - 1) * items_per_page
+    end_idx = start_idx + items_per_page
+    
+    page_df = filtered_df.iloc[start_idx:end_idx]
+    
+    def make_clickable(url):
+        return f'<a href="{url}" target="_blank" style="color: #4e79a7;">Link</a>'
+
+    page_df['job_url'] = page_df['job_url'].apply(make_clickable)
+    page_df['company_url'] = page_df['company_url'].apply(make_clickable)
+
+    st.write(page_df.to_html(escape=False, index=False), unsafe_allow_html=True)
+    
+    col1, col2, col3 = st.columns([1, 3, 1])
+    with col2:
+        st.write(f"Page {page} of {num_pages}")
 def display_about_page():
     st.markdown("""
     ## What is this application?
+
     The Job Listings Dashboard is a powerful tool designed to provide insights into the job market. It offers a comprehensive view of job postings, allowing users to explore trends, top companies, locations, and job titles.
+
     ### Key Features:
     - **Interactive Dashboard**: Visualize job market trends with dynamic charts and graphs.
     - **Data Explorer**: Dive deep into individual job listings with advanced filtering options.
     - **Real-time Data**: Fetch the latest job data from our Hugging Face dataset.
+
     ## How to use this application
+
     ### Dashboard
     1. Navigate to the Dashboard using the sidebar.
     2. View overall statistics such as total job postings, unique companies, and today's postings.
@@ -206,6 +259,7 @@ def display_about_page():
        - Job postings over time
        - Top locations for job opportunities
        - Most common job titles
+
     ### Data Explorer
     1. Switch to the Data Explorer using the sidebar.
     2. Choose between viewing all data or applying filters.
@@ -215,10 +269,13 @@ def display_about_page():
        - Job Types
     4. Browse the filtered job listings table.
     5. Click on job or company links to view more details on the original posting site.
+
     ## Data Source
     This application fetches data from my Private dataset which scrapes data from varoious job hosting portal and the data gets updated daily.
+
     ## Contact
     For questions, feedback, or collaboration opportunities, feel free to reach out:
+
     - LinkedIn: [Nihar Palem](https://www.linkedin.com/in/nihar-palem-1b955a183/)
     """)