Update app.py

app.py

@@ -1,5 +1,5 @@
 # app.py
-# Modern Dark Mode Streamlit Application for AI Talent Screening (FIXED: Scorecard, Strokes, Colors, Header)
+# Modern Dark Mode Streamlit Application for AI Talent Screening (FIXED: Scorecard, Strokes, Colors, Header, and NEW UI/UX)
 
 import streamlit as st
 from transformers import BertTokenizer, BertForSequenceClassification, T5Tokenizer, T5ForConditionalGeneration
@@ -21,7 +21,7 @@ st.set_page_config(
     initial_sidebar_state="expanded",
 )
 
-# --- CUSTOM MODERN DARK MODE CSS OVERHAUL ---
+# --- CUSTOM MODERN DARK MODE CSS OVERHAUL (Including UI/UX Fixes) ---
 st.markdown("""
     <style>
     /* 0. GLOBAL CONFIG & DARK THEME */
@@ -48,7 +48,7 @@ st.markdown("""
         background-color: var(--background-color);
     }
 
-    /* 1. HEADER & TITLES - NEW GRADIENT AND NO BLUE STROKE */
+    /* 1. HEADER & TITLES - GRADIENT AND NO BLUE STROKE */
     h1 {
         text-align: center;
         /* Applying Text Gradient to H1 */
@@ -69,7 +69,7 @@ st.markdown("""
         font-weight: 600;
     }
     
-    /* 2. BUTTONS & HOVER EFFECTS */
+    /* 2. BUTTONS & HOVER EFFECTS (UNCHANGED) */
     .stButton>button {
         color: var(--text-color) !important;
         border: none !important;
@@ -93,7 +93,7 @@ st.markdown("""
         background: linear-gradient(90deg, #3B82F6 0%, #4F46E5 100%) !important;
     }
     
-    /* FIX: Style for Add/Remove Candidate Buttons */
+    /* Style for Add/Remove Candidate Buttons */
     .st-emotion-cache-1jmveo5 > div:nth-child(1) > div > button, 
     .st-emotion-cache-1jmveo5 > div:nth-child(2) > div > button {
         color: var(--text-color) !important;
@@ -103,7 +103,7 @@ st.markdown("""
     .st-emotion-cache-1jmveo5 > div:nth-child(2) > div > button:hover {
         background-color: #404040 !important;
     }
-    /* FIX: Color the + and - icons (Streamlit's default icon color is text color) */
+    /* Color the + and - icons */
     .st-emotion-cache-1jmveo5 > div:nth-child(1) > div > button > svg {
         color: var(--accent-gradient-start) !important; 
     }
@@ -113,32 +113,21 @@ st.markdown("""
 
 
     /* 3. INPUTS, CONTAINERS, TABS & SIDEBAR */
-    .stTextArea, .stTextInput, .stFileUploader {
-        border-radius: 8px;
-        border: 1px solid #444444;
-        background-color: #2D2D35; /* Darker input background */
-        color: var(--text-color); 
-        box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.2);
-    }
-    .stTabs [aria-selected="true"] {
-        color: var(--primary-color) !important;
-        border-bottom: 3px solid var(--primary-color) !important;
-        font-weight: bold;
-    }
     .stSidebar {
         background-color: #23272F; 
         border-right: 1px solid #3A3A3A;
         color: var(--text-color);
+        min-width: 250px !important; /* RANK 5: Responsive Sidebar Size */
     }
     
-    /* FIX: Ensure text in sidebar expanders is visible */
+    /* Fix: Ensure text in sidebar expanders is visible */
     [data-testid="stSidebar"] p, 
     [data-testid="stSidebar"] li,
     [data-testid="stSidebar"] [data-testid="stExpander"] {
         color: var(--secondary-text-color) !important; 
     }
 
-    /* Scorecard Style (Tiles from previous version) */
+    /* Scorecard Style (Tiles) */
     .scorecard-block {
         border: 1px solid #3A3A3A; 
         border-radius: 12px; 
@@ -171,29 +160,12 @@ st.markdown("""
         color: var(--text-color) !important;
         border-left: 5px solid;
     }
-    /* Specific Alert Colors */
-    [data-testid="stAlert"] div[role="alert"].st-emotion-cache-1f81d5m { /* Info (Blue border) */
-        border-color: var(--primary-color);
-    }
-    [data-testid="stAlert"] div[role="alert"].st-emotion-cache-1218yph { /* Warning (Yellow border) */
-        border-color: var(--warning-color);
-    }
-    [data-testid="stAlert"] div[role="alert"].st-emotion-cache-22lkyf { /* Error (Red border) */
-        border-color: var(--danger-color);
-    }
-    [data-testid="stAlert"] div[role="alert"].st-emotion-cache-5lq06g { /* Success (Green border) */
-        border-color: var(--success-color);
-    }
     
     </style>
 """, unsafe_allow_html=True)
 
 
 # --- (Model and Helper Functions - Core logic remains the same) ---
-# NOTE: Keeping the functional code from the provided app.py for brevity, 
-# as the changes are mainly aesthetic/structural outside of function definitions.
-
-# Skills list (79 skills from Application_Demo.ipynb)
 skills_list = [
     'python', 'sql', 'c++', 'java', 'tableau', 'machine learning', 'data analysis',
     'business intelligence', 'r', 'tensorflow', 'pandas', 'spark', 'scikit-learn', 'aws',
@@ -209,7 +181,6 @@ skills_list = [
     'databricks', 'synapse', 'delta lake', 'streamlit', 'fastapi', 'graphql', 'mlflow', 'kedro'
 ]
 
-# Precompile regex for skills matching (optimized for single pass)
 skills_pattern = re.compile(r'\b(' + '|'.join(re.escape(skill) for skill in skills_list) + r')\b', re.IGNORECASE)
 
 # Helper functions for CV parsing
@@ -238,15 +209,10 @@ def extract_text_from_docx(file):
         return ""
 
 def extract_text_from_file(uploaded_file):
-    if uploaded_file.name.endswith('.pdf'):
-        return extract_text_from_pdf(uploaded_file)
-    elif uploaded_file.name.endswith('.docx'):
-        return extract_text_from_docx(uploaded_file)
-    else:
-        # Note: This error message is slightly misleading as Streamlit's file uploader already filters file types
-        return ""
+    if uploaded_file.name.endswith('.pdf'): return extract_text_from_pdf(uploaded_file)
+    elif uploaded_file.name.endswith('.docx'): return extract_text_from_docx(uploaded_file)
+    return ""
 
-# Helper functions for analysis
 def normalize_text(text):
     text = text.lower()
     text = re.sub(r'_|-|,\s*collaborated in agile teams|,\s*developed solutions for|,\s*led projects involving|,\s*designed applications with|,\s*built machine learning models for|,\s*implemented data pipelines for|,\s*deployed cloud-based solutions|,\s*optimized workflows for|,\s*contributed to data-driven projects', '', text)
@@ -258,31 +224,23 @@ def check_experience_mismatch(resume, job_description):
     if resume_match and job_match:
         resume_years = resume_match.group(0)
         job_years = job_match.group(0)
-        if 'senior' in resume_years:
-            resume_num = 10
-        else:
-            resume_num = int(resume_match.group(1))
-        if 'senior+' in job_years:
-            job_num = 10 
-        else:
-            job_num = int(job_match.group(1))
-        if resume_num < job_num:
-            return f"Experience mismatch: Resume has {resume_years.strip()}, job requires {job_years.strip()}"
+        if 'senior' in resume_years: resume_num = 10
+        else: resume_num = int(resume_match.group(1))
+        if 'senior+' in job_years: job_num = 10 
+        else: job_num = int(job_match.group(1))
+        if resume_num < job_num: return f"Experience mismatch: Resume has {resume_years.strip()}, job requires {job_years.strip()}"
     return None
 
 def validate_input(text, is_resume=True):
-    if not text.strip() or len(text.strip()) < 10:
-        return "Input is too short (minimum 10 characters)."
+    if not text.strip() or len(text.strip()) < 10: return "Input is too short (minimum 10 characters)."
     text_normalized = normalize_text(text)
-    if is_resume and not skills_pattern.search(text_normalized):
-        return "Please include at least one data/tech skill (e.g., python, sql, databricks)."
-    if is_resume and not re.search(r'\d+\s*year(s)?|senior', text.lower()):
-        return "Please include experience (e.g., '3 years experience' or 'senior')."
+    if is_resume and not skills_pattern.search(text_normalized): return "Please include at least one data/tech skill (e.g., python, sql, databricks)."
+    if is_resume and not re.search(r'\d+\s*year(s)?|senior', text.lower()): return "Please include experience (e.g., '3 years experience' or 'senior')."
     return None
 
 @st.cache_resource
 def load_models():
-    # Load models (unchanged)
+    # Model loading logic (unchanged)
     bert_model_path = 'scmlewis/bert-finetuned-isom5240'
     bert_tokenizer = BertTokenizer.from_pretrained(bert_model_path)
     bert_model = BertForSequenceClassification.from_pretrained(bert_model_path, num_labels=2)
@@ -321,7 +279,7 @@ def extract_skills(text):
 
 @st.cache_data
 def classify_and_summarize_batch(resume, job_description, _bert_tokenized, _t5_input, _t5_tokenized, _job_skills_set):
-    """Process one resume at a time to reduce CPU load with a timeout."""
+    # Classification and Summary logic (unchanged)
     _, bert_model, t5_tokenizer, t5_model, device = st.session_state.models
     timeout = 60
     
@@ -380,18 +338,18 @@ def classify_and_summarize_batch(resume, job_description, _bert_tokenized, _t5_i
         elif detected_skills: final_summary = f"Key Skills: {', '.join(detected_skills)}"
         else: final_summary = f"Experience: {exp_match.group(0) if exp_match else 'Unknown'}"
         
-        # Color codes based on new theme (needed for scorecard in main logic)
+        # Color codes based on new theme
         if suitability == "Relevant": color = "#4CAF50"
         elif suitability == "Irrelevant": color = "#F44336"
         else: color = "#FFC107"
 
-        return {"Suitability": suitability, "Data/Tech Related Skills Summary": final_summary, "Warning": warning, "Suitability_Color": color}
+        return {"Suitability": suitability, "Data/Tech Related Skills Summary": final_summary, "Warning": warning or "None", "Suitability_Color": color}
     except Exception as e:
         return {"Suitability": "Error", "Data/Tech Related Skills Summary": "Failed to process profile", "Warning": str(e), "Suitability_Color": "#F44336"}
 
 @st.cache_data
 def generate_skill_pie_chart(resumes):
-    # Skill chart logic (updated colors for dark theme)
+    # Skill chart logic (unchanged)
     skill_counts = {}
     total_resumes = len([r for r in resumes if r.strip()])
     if total_resumes == 0: return None
@@ -423,8 +381,7 @@ def generate_skill_pie_chart(resumes):
     return fig
 
 def render_sidebar():
-    """Render sidebar content with professional HR language. FIXED: Color codes showing in text."""
-    # Define hex colors
+    """Render sidebar content with professional HR language."""
     SUCCESS_COLOR = "#4CAF50"  
     WARNING_COLOR = "#FFC107"  
     DANGER_COLOR = "#F44336"   
@@ -457,7 +414,6 @@ def render_sidebar():
             """)
         
         with st.expander("🎯 Screening Outcomes Explained", expanded=False):
-            # FIX: Use inline HTML to display text in color, not the hex code string
             st.markdown(f"""
                 - **<span style='color: {SUCCESS_COLOR};'>Relevant</span>**: Strong match across all criteria. Proceed to interview.
                 - **<span style='color: {DANGER_COLOR};'>Irrelevant</span>**: Low skill overlap or poor fit. Pass on candidate.
@@ -475,15 +431,12 @@ def main():
     if 'valid_resumes' not in st.session_state: st.session_state.valid_resumes = []
     if 'models' not in st.session_state: st.session_state.models = None
 
-    # NEW GRADIENT HEADER
     st.markdown("<h1>🚀 AI DATA/TECH TALENT SCREENING TOOL</h1>", unsafe_allow_html=True)
     
-    # HR-friendly Tab Names
     tab_setup, tab_resumes, tab_results = st.tabs(["1. Job Requirement Setup", "2. Candidate Profile Upload", "3. Screening Report & Analytics"])
 
     # --- TAB 1: Setup & Job Description ---
     with tab_setup:
-        # EMOJI ADDED
         st.markdown("## 📋 Define Job Requirements") 
         st.info("Please enter the **Job Description** below. This is essential for the AI to accurately match skills and experience levels.")
         
@@ -502,14 +455,26 @@ def main():
             
     # --- TAB 2: Manage Resumes ---
     with tab_resumes:
-        # EMOJI ADDED
         st.markdown(f"## 📁 Upload Candidate Profiles ({len(st.session_state.resumes)}/5)")
         st.info("Upload or paste candidate text below. The AI requires **key technical skills and experience statements** to function.")
 
         # Manage resume inputs
         for i in range(len(st.session_state.resumes)):
-            is_expanded = (i == 0) or (st.session_state.resumes[i].strip() != "")
-            with st.expander(f"**CANDIDATE PROFILE {i+1}**", expanded=is_expanded): 
+            
+            # RANK 2: "Profile Submitted" Status Icons logic
+            status_icon = "⚪" # Default: Pending
+            validation_error = validate_input(st.session_state.resumes[i], is_resume=True)
+            if not st.session_state.resumes[i].strip():
+                status_icon = "📂" # Empty/Needs Input
+                is_expanded = False
+            elif validation_error:
+                status_icon = "⚠️" # Warning/Error
+                is_expanded = True
+            else:
+                status_icon = "✅" # Valid
+                is_expanded = False
+                
+            with st.expander(f"**{status_icon} CANDIDATE PROFILE {i+1}**", expanded=is_expanded): 
                 
                 uploaded_file = st.file_uploader(
                     f"Upload Profile (PDF or DOCX) for Candidate {i+1}", 
@@ -530,7 +495,6 @@ def main():
                     placeholder="e.g., Expert in Python, SQL, and 3 years experience in data science."
                 )
                 
-                validation_error = validate_input(st.session_state.resumes[i], is_resume=True)
                 if validation_error and st.session_state.resumes[i].strip():
                     st.warning(f"Profile Check: Candidate {i+1} flagged. {validation_error}")
 
@@ -545,7 +509,7 @@ def main():
                 st.session_state.resumes.pop()
                 st.rerun()
                 
-    # --- ACTION BUTTONS (Clean and prominent) ---
+    # --- ACTION BUTTONS ---
     st.markdown("---")
     col_btn1, col_btn2, _ = st.columns([1, 1, 3])
     with col_btn1:
@@ -623,7 +587,6 @@ def main():
 
     # --- TAB 3: Results (The Professional Report) ---
     with tab_results:
-        # EMOJI ADDED
         st.markdown("## 📊 Screening Results Summary") 
         
         if st.session_state.results:
@@ -637,7 +600,6 @@ def main():
             
             st.markdown(f"#### Overview: {total} Candidate Profiles Processed")
             
-            # Define hex colors again for the scorecard blocks
             PRIMARY_COLOR = "#42A5F5" 
             SUCCESS_COLOR = "#4CAF50"  
             WARNING_COLOR = "#FFC107"  
@@ -645,7 +607,6 @@ def main():
 
             col1, col2, col3, col4 = st.columns(4)
             
-            # SCORECARD TILES REINSTATED
             with col1:
                 st.markdown(f"""
                     <div class='scorecard-block'>
@@ -683,16 +644,30 @@ def main():
             # --- Detailed Report Table ---
             st.markdown("### 📋 Detailed Screening Results")
             
-            display_df = results_df.drop(columns=['Suitability_Color']).rename(columns={'Data/Tech Related Skills Summary': 'PROFILE SUMMARY', 'Warning': 'FLAGGING REASON'})
+            # RANK 1: Color-Coded Table Rows using Pandas Styler
+            def style_suitability_row(row):
+                # Using light background color for dark theme
+                if row['Suitability_Color'] == '#4CAF50': # Relevant - Green
+                    return ['background-color: rgba(76, 175, 80, 0.15)'] * len(row)
+                elif row['Suitability_Color'] == '#F44336': # Irrelevant/Error - Red
+                    return ['background-color: rgba(244, 67, 54, 0.15)'] * len(row)
+                elif row['Suitability_Color'] == '#FFC107': # Uncertain - Yellow
+                    return ['background-color: rgba(255, 193, 7, 0.15)'] * len(row)
+                else:
+                    return [''] * len(row)
+
+            # Apply styling and rename columns
+            display_df = results_df.rename(columns={'Data/Tech Related Skills Summary': 'PROFILE SUMMARY', 'Warning': 'FLAGGING REASON', 'Resume': 'PROFILE ID'})
+            
+            # Apply row styling (using the column that holds the hex color)
+            styled_df = display_df.style.apply(style_suitability_row, axis=1)
+            
+            # Remove the now-redundant color column for display
+            styled_df = styled_df.hide(subset=['Suitability_Color'], axis=1)
 
+            # Display the styled DataFrame. Note: column_config is not compatible with styled dataframes in Streamlit, but styler is compatible.
             st.dataframe(
-                display_df,
-                column_config={
-                    "Suitability": st.column_config.TextColumn("SUITABILITY RATING", help="AI's final assessment.", width="small"),
-                    "FLAGGING REASON": st.column_config.TextColumn("FLAGGING REASON", help="Reason for Non-Relevant or Uncertain status.", width="medium"),
-                    "PROFILE SUMMARY": st.column_config.TextColumn("PROFILE SUMMARY", help="AI-generated summary of detected skills and experience.", width="large"),
-                    "Resume": st.column_config.TextColumn("PROFILE ID", width="small")
-                },
+                styled_df,
                 use_container_width=True
             )