Update app.py

app.py

@@ -323,12 +323,14 @@ def get_job_matches(dream_job: str, top_n: int, skills_text: str):
     emb_matches = find_job_matches(dream_job, expanded_desc, top_k=50)
     user_skills = [_norm_skill_token(s) for s in skills_text.split(',') if _norm_skill_token(s)]
     
+    # --- NEW: Initialize variables for the recommendations section ---
     recommendations_table = pd.DataFrame()
     recommendations_visible = False
     
     if user_skills:
         scored_df = score_jobs_by_skills(user_skills, emb_matches)
         
+        # --- NEW: Logic to get top 5 jobs based purely on skill match score ---
         skill_sorted_df = scored_df.sort_values(by='Skill Match Score', ascending=False).head(5)
         if not skill_sorted_df.empty:
             recs = skill_sorted_df[['job_title', 'company', 'Skill Match Score', 'Final Score']].copy()
@@ -337,6 +339,7 @@ def get_job_matches(dream_job: str, top_n: int, skills_text: str):
             recs['Overall Score'] = recs['Overall Score'].map('{:.2%}'.format)
             recommendations_table = recs
             recommendations_visible = True
+        # --- END NEW ---
 
         display_df = scored_df.head(top_n)
         status = f"Found and **re-ranked** results by your {len(user_skills)} skills. Displaying top {len(display_df)}."
@@ -357,6 +360,7 @@ def get_job_matches(dream_job: str, top_n: int, skills_text: str):
     dropdown_options = [(f"{i+1}. {row['job_title']} - {row['company']}", row.name) for i, row in display_df.iterrows()]
     dropdown_value = dropdown_options[0][1] if dropdown_options else None
     
+    # --- MODIFIED: Added new outputs for recommendations ---
     return status, emb_matches, table_to_show, gr.Dropdown(choices=dropdown_options, value=dropdown_value, visible=True), gr.Accordion(visible=True), recommendations_table, gr.Accordion(visible=recommendations_visible)
 
 def rerank_current_results(initial_matches_df, skills_text, top_n):
@@ -365,6 +369,7 @@ def rerank_current_results(initial_matches_df, skills_text, top_n):
     initial_matches_df = pd.DataFrame(initial_matches_df)
     user_skills = [_norm_skill_token(s) for s in skills_text.split(',') if _norm_skill_token(s)]
     
+    # --- NEW: Initialize variables for the recommendations section ---
     recommendations_table = pd.DataFrame()
     recommendations_visible = False
 
@@ -379,6 +384,7 @@ def rerank_current_results(initial_matches_df, skills_text, top_n):
         status = f"Results **re-ranked** based on your {len(user_skills)} skills."
         display_df = ranked_df.head(top_n)
 
+        # --- NEW: Logic to get top 5 jobs based purely on skill match score ---
         skill_sorted_df = ranked_df.sort_values(by='Skill Match Score', ascending=False).head(5)
         if not skill_sorted_df.empty:
             recs = skill_sorted_df[['job_title', 'company', 'Skill Match Score', 'Final Score']].copy()
@@ -387,6 +393,7 @@ def rerank_current_results(initial_matches_df, skills_text, top_n):
             recs['Overall Score'] = recs['Overall Score'].map('{:.2%}'.format)
             recommendations_table = recs
             recommendations_visible = True
+        # --- END NEW ---
             
         table_to_show = display_df[['job_title', 'company', 'Final Score', 'Skill Match Score']]
         table_to_show = table_to_show.rename(columns={'Final Score': 'Overall Score'})
@@ -396,15 +403,18 @@ def rerank_current_results(initial_matches_df, skills_text, top_n):
     dropdown_options = [(f"{i+1}. {row['job_title']} - {row['company']}", row.name) for i, row in display_df.iterrows()]
     dropdown_value = dropdown_options[0][1] if dropdown_options else None
     
+    # --- MODIFIED: Added new outputs for recommendations ---
     return status, table_to_show, gr.Dropdown(choices=dropdown_options, value=dropdown_value, visible=True), recommendations_table, gr.Accordion(visible=recommendations_visible)
 
 def find_matches_and_rank_with_check(dream_job: str, top_n: int, skills_text: str):
     if not dream_job:
+        # --- MODIFIED: Added new default outputs ---
         return "Please describe your dream job first.", None, pd.DataFrame(), gr.Dropdown(visible=False), gr.Accordion(visible=False), gr.Markdown(""), gr.Row(visible=False), pd.DataFrame(), gr.Accordion(visible=False)
     unrecognized_words = check_spelling_in_query(dream_job)
     if unrecognized_words:
         word_list_html = ", ".join([f"<b><span style='color: #F87171;'>{w}</span></b>" for w in unrecognized_words])
         alert_message = f"<b><span style='color: #F87171;'>⚠️ Possible Spelling Error:</span></b> Unrecognized: {word_list_html}."
+        # --- MODIFIED: Added new default outputs ---
         return "Status: Awaiting confirmation.", None, pd.DataFrame(), gr.Dropdown(visible=False), gr.Accordion(visible=False), gr.Markdown(alert_message, visible=True), gr.Row(visible=True), pd.DataFrame(), gr.Accordion(visible=False)
     
     status, emb_matches, table_to_show, dropdown, details_accordion, recommendations_table, recommendations_accordion = get_job_matches(dream_job, top_n, skills_text)
@@ -475,6 +485,7 @@ def load_more_skills(full_skills_list, current_offset):
     return learning_plan_html, new_offset, gr.Button(visible=should_button_be_visible)
 
 def on_reset():
+    # --- MODIFIED: Added new default outputs for reset ---
     return ("", 3, "", pd.DataFrame(), None, gr.Dropdown(visible=False), gr.Accordion(visible=False), "Status: Ready.", "", "", "", "", gr.Markdown(visible=False), gr.Row(visible=False), [], 0, gr.Button(visible=False), pd.DataFrame(), gr.Accordion(visible=False))
 
 print("Starting application initialization...")
@@ -505,6 +516,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as ui:
     
     df_output = gr.DataFrame(label="Job Matches (Sorted by Overall Relevance)", interactive=False)
 
+    # --- NEW: Added the recommendations section ---
     with gr.Accordion("✨ Based on your current skills and career interest consider these jobs...", open=True, visible=False) as recommendations_accordion:
         recommendations_df_output = gr.DataFrame(label="Top Skill Matches", interactive=False)
 
@@ -518,6 +530,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as ui:
         learning_plan_output = gr.HTML(label="Learning Plan")
         load_more_btn = gr.Button("Load More Skills", visible=False)
 
+    # --- MODIFIED: Added new outputs to the click events ---
     search_btn.click(fn=find_matches_and_rank_with_check, inputs=[dream_text, topk_slider, skills_text], outputs=[status_text, initial_matches_state, df_output, job_selector, details_accordion, spelling_alert, spelling_row, recommendations_df_output, recommendations_accordion])
     search_anyway_btn.click(fn=find_matches_and_rank_anyway, inputs=[dream_text, topk_slider, skills_text], outputs=[status_text, initial_matches_state, df_output, job_selector, details_accordion, spelling_alert, spelling_row, recommendations_df_output, recommendations_accordion])
     retype_btn.click(lambda: ("Status: Ready for you to retype.", None, pd.DataFrame(), gr.Dropdown(visible=False), gr.Accordion(visible=False), gr.Markdown(visible=False), gr.Row(visible=False), pd.DataFrame(), gr.Accordion(visible=False)), outputs=[status_text, initial_matches_state, df_output, job_selector, details_accordion, spelling_alert, spelling_row, recommendations_df_output, recommendations_accordion])