Update app.py

app.py

@@ -1,6 +1,11 @@
-import os, io, re, time, tempfile
+import os
+import io
 import streamlit as st
-import docx, docx2txt
+import docx
+import docx2txt
+import tempfile
+import time
+import re
 import pandas as pd
 from functools import lru_cache
 
@@ -12,21 +17,24 @@ except ImportError:
     from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForSeq2SeqLM
     import torch
     has_pipeline = False
+    st.warning("Using basic transformers functionality instead of pipeline API")
 
-# Setup page
+# Set page title and hide sidebar
 st.set_page_config(page_title="Resume-Job Fit Analyzer", initial_sidebar_state="collapsed")
-st.markdown("""<style>[data-testid="collapsedControl"],[data-testid="stSidebar"] {display: none;}</style>""", unsafe_allow_html=True)
+st.markdown("""<style>[data-testid="collapsedControl"] {display: none;}section[data-testid="stSidebar"] {display: none;}</style>""", unsafe_allow_html=True)
 
 #####################################
-# Model Loading & Text Processing
+# Preload Models & Helper Functions
 #####################################
-@st.cache_resource
+@st.cache_resource(show_spinner=True)
 def load_models():
-    with st.spinner("Loading AI models..."):
+    """Load models at startup"""
+    with st.spinner("Loading AI models... This may take a minute on first run."):
         models = {}
+        
         # Load summarization model
         if has_pipeline:
-            models['summarizer'] = pipeline("summarization", model="Falconsai/text_summarization", max_length=100)
+            models['summarizer'] = pipeline("summarization", model="Falconsai/text_summarization", max_length=100, truncation=True)
         else:
             try:
                 models['summarizer_model'] = AutoModelForSeq2SeqLM.from_pretrained("Falconsai/text_summarization")
@@ -45,331 +53,453 @@ def load_models():
             except Exception as e:
                 st.error(f"Error loading sentiment model: {e}")
                 models['evaluator_model'] = models['evaluator_tokenizer'] = None
+        
         return models
 
 def summarize_text(text, models, max_length=100):
-    """Summarize text with fallbacks"""
+    """Summarize text using available models with fallbacks"""
+    # Truncate input to prevent issues with long texts
     input_text = text[:1024]
     
-    # Try pipeline
+    # Try pipeline first
     if has_pipeline and 'summarizer' in models:
         try:
             return models['summarizer'](input_text)[0]['summary_text']
-        except: pass
+        except Exception as e:
+            st.warning(f"Error in pipeline summarization: {e}")
     
     # Try manual model
-    if 'summarizer_model' in models and models['summarizer_model']:
+    if 'summarizer_model' in models and 'summarizer_tokenizer' in models and models['summarizer_model']:
         try:
             tokenizer = models['summarizer_tokenizer']
             model = models['summarizer_model']
             inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=1024)
-            summary_ids = model.generate(inputs.input_ids, max_length=max_length, min_length=30, num_beams=4)
+            summary_ids = model.generate(inputs.input_ids, max_length=max_length, min_length=30, num_beams=4, early_stopping=True)
             return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-        except: pass
+        except Exception as e:
+            st.warning(f"Error in manual summarization: {e}")
     
-    # Fallback - extract sentences
+    # Fallback to basic summarization
+    return basic_summarize(text, max_length)
+
+def basic_summarize(text, max_length=100):
+    """Basic extractive text summarization"""
     sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
-    scored = [(1.0/(i+1), s) for i, s in enumerate(sentences) if len(s.split()) >= 4]
-    scored.sort(reverse=True)
-    
-    result, length = [], 0
-    for _, sentence in scored:
-        if length + len(sentence.split()) <= max_length:
-            result.append(sentence)
-            length += len(sentence.split())
-    
-    if result:
-        ordered = sorted([(sentences.index(s), s) for s in result])
-        return " ".join(s for _, s in ordered)
-    return ""
+    
+    # Score and filter sentences
+    scored_sentences = []
+    for i, sentence in enumerate(sentences):
+        if len(sentence.split()) >= 4:
+            score = 1.0 / (i + 1) - (0.01 * max(0, len(sentence.split()) - 20))
+            scored_sentences.append((score, sentence))
+    
+    # Get top sentences
+    scored_sentences.sort(reverse=True)
+    summary_sentences = []
+    current_length = 0
+    
+    for _, sentence in scored_sentences:
+        if current_length + len(sentence.split()) <= max_length:
+            summary_sentences.append(sentence)
+            current_length += len(sentence.split())
+        else:
+            break
+    
+    # Restore original sentence order
+    if summary_sentences:
+        original_order = [(sentences.index(s), s) for s in summary_sentences]
+        original_order.sort()
+        summary_sentences = [s for _, s in original_order]
+    
+    return " ".join(summary_sentences)
 
 #####################################
-# File Processing & Information Extraction
+# Information Extraction Functions
 #####################################
-@st.cache_data
+@st.cache_data(show_spinner=False)
 def extract_text_from_file(file_obj):
-    ext = os.path.splitext(file_obj.name)[1].lower()
+    """Extract text from uploaded document file"""
+    filename = file_obj.name
+    ext = os.path.splitext(filename)[1].lower()
     
     if ext == ".docx":
         try:
             document = docx.Document(file_obj)
-            return "\n".join(para.text for para in document.paragraphs if para.text.strip())[:15000]
+            text = "\n".join(para.text for para in document.paragraphs if para.text.strip())
         except Exception as e:
             return f"Error processing DOCX file: {e}"
     elif ext == ".doc":
         try:
             with tempfile.NamedTemporaryFile(delete=False, suffix='.doc') as temp_file:
                 temp_file.write(file_obj.getvalue())
-                text = docx2txt.process(temp_file.name)
-                os.unlink(temp_file.name)
-                return text[:15000]
+                temp_path = temp_file.name
+            
+            text = docx2txt.process(temp_path)
+            os.unlink(temp_path)
         except Exception as e:
             return f"Error processing DOC file: {e}"
     elif ext == ".txt":
         try:
-            return file_obj.getvalue().decode("utf-8")[:15000]
+            text = file_obj.getvalue().decode("utf-8")
         except Exception as e:
             return f"Error processing TXT file: {e}"
     else:
         return "Unsupported file type. Please upload a .docx, .doc, or .txt file."
+    
+    return text[:15000] if text else text
 
-# Information extraction functions
 def extract_skills(text):
-    """Extract skills from text"""
+    """Extract key skills from the resume"""
     skill_keywords = {
-        "Programming": ["Python", "Java", "JavaScript", "HTML", "CSS", "SQL", "C++", "C#", "React", "Angular"],
-        "Data Science": ["Machine Learning", "Data Analysis", "Statistics", "TensorFlow", "PyTorch", "AI", "NLP"],
-        "Database": ["SQL", "MySQL", "MongoDB", "PostgreSQL", "Oracle", "Redis"],
-        "Web Dev": ["React", "Angular", "Node.js", "Frontend", "Backend", "Full-Stack", "REST API"],
-        "Software Dev": ["Agile", "Scrum", "Git", "DevOps", "Docker", "CI/CD", "Jenkins"],
-        "Cloud": ["AWS", "Azure", "Google Cloud", "Lambda", "S3", "EC2"],
-        "Business": ["Project Management", "Leadership", "Teamwork", "Agile", "Scrum"]
+        "Programming": ["Python", "Java", "JavaScript", "HTML", "CSS", "SQL", "C++", "C#", "Go", "React", "Angular", "Vue", "Node.js"],
+        "Data Science": ["Machine Learning", "Data Analysis", "Statistics", "TensorFlow", "PyTorch", "AI", "Algorithms", "NLP", "Deep Learning"],
+        "Database": ["SQL", "MySQL", "MongoDB", "Database", "NoSQL", "PostgreSQL", "Oracle", "Redis"],
+        "Web Development": ["React", "Angular", "Node.js", "Frontend", "Backend", "Full-Stack", "REST API", "GraphQL"],
+        "Software Development": ["Agile", "Scrum", "Git", "DevOps", "Docker", "System Design", "CI/CD", "Jenkins"],
+        "Cloud": ["AWS", "Azure", "Google Cloud", "Cloud Computing", "Lambda", "S3", "EC2"],
+        "Security": ["Cybersecurity", "Network Security", "Encryption", "Security"],
+        "Business": ["Project Management", "Business Analysis", "Leadership", "Teamwork", "Agile", "Scrum"],
+        "Design": ["UX/UI", "User Experience", "Design Thinking", "Adobe", "Figma"]
     }
     
     text_lower = text.lower()
-    return [skill for _, skills in skill_keywords.items() for skill in skills if skill.lower() in text_lower]
+    return [skill for category, skills in skill_keywords.items() 
+            for skill in skills if skill.lower() in text_lower]
 
 @lru_cache(maxsize=32)
 def extract_name(text_start):
-    lines = [line.strip() for line in text_start.split('\n')[:5] if line.strip()]
+    """Extract candidate name from the beginning of resume text"""
+    lines = text_start.split('\n')
+    potential_name_lines = [line.strip() for line in lines[:5] if line.strip()]
     
-    if lines:
-        first_line = lines[0]
-        if 5 <= len(first_line) <= 40 and not any(x in first_line.lower() for x in ["resume", "cv", "curriculum", "vitae"]):
+    if potential_name_lines:
+        first_line = potential_name_lines[0]
+        if 5 <= len(first_line) <= 40 and not any(x in first_line.lower() for x in ["resume", "cv", "curriculum", "vitae", "profile"]):
             return first_line
     
-    for line in lines[:3]:
+    for line in potential_name_lines[:3]:
         if len(line.split()) <= 4 and not any(x in line.lower() for x in ["address", "phone", "email", "resume", "cv"]):
             return line
-    return "Unknown"
+
+    return "Unknown (please extract from resume)"
 
 def extract_age(text):
-    for pattern in [r'age:?\s*(\d{1,2})', r'(\d{1,2})\s*years\s*old', r'dob:.*(\d{4})', r'date of birth:.*(\d{4})']:
-        match = re.search(pattern, text.lower())
-        if match:
-            if len(match.group(1)) == 4:  # Birth year
-                try: return str(2025 - int(match.group(1)))
-                except: pass
-            return match.group(1)
+    """Extract candidate age from resume text"""
+    age_patterns = [
+        r'age:?\s*(\d{1,2})',
+        r'(\d{1,2})\s*years\s*old',
+        r'dob:.*(\d{4})',
+        r'date of birth:.*(\d{4})'
+    ]
+    
+    text_lower = text.lower()
+    for pattern in age_patterns:
+        matches = re.search(pattern, text_lower)
+        if matches:
+            # Convert birth year to age if needed
+            if len(matches.group(1)) == 4:
+                try:
+                    return str(2025 - int(matches.group(1)))
+                except:
+                    pass
+            return matches.group(1)
+    
     return "Not specified"
 
 def extract_industry(text):
-    industries = {
+    """Extract expected job industry from resume"""
+    industry_keywords = {
         "Technology": ["software", "programming", "developer", "IT", "tech", "computer", "digital"],
         "Finance": ["banking", "financial", "accounting", "finance", "analyst"],
-        "Healthcare": ["medical", "health", "hospital", "clinical", "nurse", "doctor"],
-        "Education": ["teaching", "teacher", "professor", "education", "university", "school"],
+        "Healthcare": ["medical", "health", "hospital", "clinical", "nurse", "doctor", "patient"],
+        "Education": ["teaching", "teacher", "professor", "education", "university", "school", "academic"],
         "Marketing": ["marketing", "advertising", "digital marketing", "social media", "brand"],
         "Engineering": ["engineer", "engineering", "mechanical", "civil", "electrical"],
         "Data Science": ["data science", "machine learning", "AI", "analytics", "big data"],
-        "Management": ["manager", "management", "leadership", "executive", "director"]
+        "Management": ["manager", "management", "leadership", "executive", "director"],
+        "Consulting": ["consultant", "consulting", "advisor"],
+        "Sales": ["sales", "business development", "account manager", "client relations"]
     }
     
     text_lower = text.lower()
-    counts = {ind: sum(text_lower.count(kw) for kw in kws) for ind, kws in industries.items()}
-    return max(counts.items(), key=lambda x: x[1])[0] if any(counts.values()) else "Not specified"
+    industry_counts = {industry: sum(text_lower.count(keyword.lower()) for keyword in keywords) 
+                       for industry, keywords in industry_keywords.items()}
+    
+    return max(industry_counts.items(), key=lambda x: x[1])[0] if any(industry_counts.values()) else "Not clearly specified"
 
 def extract_job_position(text):
+    """Extract expected job position from resume"""
+    objective_patterns = [
+        r'objective:?\s*(.*?)(?=\n\n|\n\w+:|\Z)',
+        r'career\s*objective:?\s*(.*?)(?=\n\n|\n\w+:|\Z)',
+        r'professional\s*summary:?\s*(.*?)(?=\n\n|\n\w+:|\Z)',
+        r'summary:?\s*(.*?)(?=\n\n|\n\w+:|\Z)',
+        r'seeking\s*(?:a|an)?\s*(?:position|role|opportunity)\s*(?:as|in)?\s*(?:a|an)?\s*([^.]*)'
+    ]
+    
     text_lower = text.lower()
-    for pattern in [r'objective:?\s*(.*?)(?=\n\n|\n\w+:|\Z)', r'career\s*objective:?\s*(.*?)(?=\n\n|\n\w+:|\Z)', 
-                   r'summary:?\s*(.*?)(?=\n\n|\n\w+:|\Z)', r'seeking.*position.*as\s*([^.]*)']:
+    for pattern in objective_patterns:
         match = re.search(pattern, text_lower, re.IGNORECASE | re.DOTALL)
         if match:
-            text = match.group(1).strip()
-            for title in ["developer", "engineer", "analyst", "manager", "specialist", "designer"]:
-                if title in text:
-                    return next((m.group(1).strip().title() for m in 
-                               [re.search(r'(\w+\s+' + title + r')', text)] if m), title.title())
-            return " ".join(text.split()[:10]).title() + "..." if len(text.split()) > 10 else text.title()
-    
-    # Check for job title near experience
-    for pattern in [r'experience:.*?(\w+\s+\w+(?:\s+\w+)?)(?=\s*at|\s*\()', r'(\w+\s+\w+(?:\s+\w+)?)\s*\(\s*(?:current|present)']:
+            objective_text = match.group(1).strip()
+            job_titles = ["developer", "engineer", "analyst", "manager", "director", "specialist", 
+                          "coordinator", "consultant", "designer", "architect", "administrator"]
+            
+            for title in job_titles:
+                if title in objective_text:
+                    title_pattern = r'(?:a|an)?\s*(\w+\s+' + title + r'|\w+\s+\w+\s+' + title + r')'
+                    title_match = re.search(title_pattern, objective_text)
+                    if title_match:
+                        return title_match.group(1).strip().title()
+                    return title.title()
+            
+            if len(objective_text) > 10:
+                words = objective_text.split()
+                return " ".join(words[:10]).title() + "..." if len(words) > 10 else objective_text.title()
+    
+    job_patterns = [
+        r'experience:.*?(\w+\s+\w+(?:\s+\w+)?)(?=\s*at|\s*\(|\s*-|\s*,|\s*\d{4}|\n)',
+        r'(\w+\s+\w+(?:\s+\w+)?)\s*\(\s*current\s*\)',
+        r'(\w+\s+\w+(?:\s+\w+)?)\s*\(\s*present\s*\)'
+    ]
+    
+    for pattern in job_patterns:
         match = re.search(pattern, text_lower, re.IGNORECASE)
-        if match: return match.group(1).strip().title()
+        if match:
+            return match.group(1).strip().title()
     
-    return "Not specified"
+    return "Not explicitly stated"
 
 #####################################
 # Core Analysis Functions
 #####################################
 def summarize_resume_text(resume_text, models):
-    start = time.time()
+    """Generate a structured summary of resume text"""
+    start_time = time.time()
     
-    # Basic info extraction
+    # Extract critical information
     name = extract_name(resume_text[:500])
     age = extract_age(resume_text)
     industry = extract_industry(resume_text)
     job_position = extract_job_position(resume_text)
     skills = extract_skills(resume_text)
     
-    # Generate summary
+    # Generate overall summary
     try:
         if has_pipeline and 'summarizer' in models:
-            model_summary = models['summarizer'](resume_text[:2000], max_length=100, min_length=30)[0]['summary_text']
+            model_summary = models['summarizer'](resume_text[:2000], max_length=100, min_length=30, do_sample=False)[0]['summary_text']
         else:
             model_summary = summarize_text(resume_text, models, max_length=100)
-    except:
-        model_summary = "Error generating summary."
-    
-    # Format result
-    summary = f"Name: {name}\n\nAge: {age}\n\nExpected Industry: {industry}\n\n"
-    summary += f"Expected Job Position: {job_position}\n\nSkills: {', '.join(skills)}\n\nSummary: {model_summary}"
-    
-    return summary, time.time() - start
+    except Exception as e:
+        st.warning(f"Error in resume summarization: {e}")
+        model_summary = "Error generating summary. Please check the original resume."
+    
+    # Format the structured summary
+    formatted_summary = f"Name: {name}\n\n"
+    formatted_summary += f"Age: {age}\n\n"
+    formatted_summary += f"Expected Industry: {industry}\n\n"
+    formatted_summary += f"Expected Job Position: {job_position}\n\n"
+    formatted_summary += f"Skills: {', '.join(skills)}\n\n"
+    formatted_summary += f"Summary: {model_summary}"
+    
+    return formatted_summary, time.time() - start_time
 
 def extract_job_requirements(job_description, models):
+    """Extract key requirements from a job description"""
+    # Combined skill list (abridged for brevity)
     tech_skills = [
-        "Python", "Java", "JavaScript", "SQL", "HTML", "CSS", "React", "Angular", "Machine Learning", "AWS",
-        "Azure", "Docker", "MySQL", "MongoDB", "Project Management", "Agile", "Leadership", "Git", "DevOps"
+        "Python", "Java", "C++", "JavaScript", "TypeScript", "SQL", "HTML", "CSS", "React", "Angular", 
+        "Machine Learning", "Data Science", "AI", "AWS", "Azure", "Docker", "Kubernetes", "MySQL", 
+        "MongoDB", "PostgreSQL", "Project Management", "Agile", "Scrum", "Leadership", "Communication",
+        "Problem Solving", "Git", "DevOps", "Full Stack", "Mobile Development", "Android", "iOS"
     ]
     
-    clean_text = job_description.lower()
+    clean_job_text = job_description.lower()
     
     # Extract job title
+    title_patterns = [
+        r'^([^:.\n]+?)(position|role|job|opening|vacancy)',
+        r'^([^:.\n]+?)\n',
+        r'(hiring|looking for(?: a| an)?|recruiting)(?: a| an)? ([^:.\n]+?)(:-|[.:]|\n|$)'
+    ]
+    
     job_title = "Not specified"
-    for pattern in [r'^([^:.\n]+?)(position|role|job)', r'^([^:.\n]+?)\n', r'hiring.*? ([^:.\n]+?)(:-|[.:]|\n|$)']:
-        match = re.search(pattern, clean_text, re.IGNORECASE)
-        if match:
-            title = match.group(1).strip() if len(match.groups()) >= 1 else match.group(2).strip()
-            if 3 <= len(title) <= 50:
-                job_title = title.capitalize()
+    for pattern in title_patterns:
+        title_match = re.search(pattern, clean_job_text, re.IGNORECASE)
+        if title_match:
+            potential_title = title_match.group(1).strip() if len(title_match.groups()) >= 1 else title_match.group(2).strip()
+            if 3 <= len(potential_title) <= 50:
+                job_title = potential_title.capitalize()
                 break
     
-    # Extract years required
+    # Extract years of experience
+    exp_patterns = [
+        r'(\d+)(?:\+)?\s*(?:years|yrs)(?:\s*of)?\s*(?:experience|exp)',
+        r'experience\s*(?:of)?\s*(\d+)(?:\+)?\s*(?:years|yrs)'
+    ]
+    
     years_required = 0
-    for pattern in [r'(\d+)(?:\+)?\s*(?:years|yrs).*?experience', r'experience.*?(\d+)(?:\+)?\s*(?:years|yrs)']:
-        match = re.search(pattern, clean_text, re.IGNORECASE)
-        if match:
+    for pattern in exp_patterns:
+        exp_match = re.search(pattern, clean_job_text, re.IGNORECASE)
+        if exp_match:
             try:
-                years_required = int(match.group(1))
+                years_required = int(exp_match.group(1))
                 break
-            except: pass
+            except:
+                pass
     
-    # Extract skills
-    required_skills = [skill for skill in tech_skills if re.search(r'\b' + re.escape(skill.lower()) + r'\b', clean_text)]
+    # Extract required skills
+    required_skills = [skill for skill in tech_skills if re.search(r'\b' + re.escape(skill.lower()) + r'\b', clean_job_text)]
     
     # Fallback if no skills found
     if not required_skills:
-        words = [w for w in re.findall(r'\b\w{4,}\b', clean_text) 
-                if w not in ["with", "that", "this", "have", "from", "they", "will", "what", "your"]]
+        words = re.findall(r'\b\w{4,}\b', clean_job_text)
         word_counts = {}
-        for w in words: word_counts[w] = word_counts.get(w, 0) + 1
-        required_skills = [w.capitalize() for w, _ in sorted(word_counts.items(), key=lambda x: x[1], reverse=True)[:5]]
+        for word in words:
+            if word not in ["with", "that", "this", "have", "from", "they", "will", "what", "your", "their", "about"]:
+                word_counts[word] = word_counts.get(word, 0) + 1
+        sorted_words = sorted(word_counts.items(), key=lambda x: x[1], reverse=True)
+        required_skills = [word.capitalize() for word, _ in sorted_words[:5]]
+    
+    job_summary = summarize_text(job_description, models, max_length=100)
     
     return {
         "title": job_title,
         "years_experience": years_required,
         "required_skills": required_skills,
-        "summary": summarize_text(job_description, models, max_length=100)
+        "summary": job_summary
     }
 
 def evaluate_job_fit(resume_summary, job_requirements, models):
-    start = time.time()
+    """Evaluate how well a resume matches job requirements"""
+    start_time = time.time()
     
-    # Basic extraction
+    # Extract information
     required_skills = job_requirements["required_skills"]
     years_required = job_requirements["years_experience"]
     job_title = job_requirements["title"]
     skills_mentioned = extract_skills(resume_summary)
     
-    # Calculate matches
+    # Calculate match percentages
     matching_skills = [skill for skill in required_skills if skill in skills_mentioned]
-    skill_match = len(matching_skills) / len(required_skills) if required_skills else 0
+    skill_match_percentage = len(matching_skills) / len(required_skills) if required_skills else 0
     
-    # Extract experience
+    # Extract experience level from resume
+    experience_pattern = r'(\d+)\+?\s*years?\s*(?:of)?\s*experience'
     years_experience = 0
-    exp_match = re.search(r'(\d+)\+?\s*years?\s*(?:of)?\s*experience', resume_summary, re.IGNORECASE)
-    if exp_match:
-        try: years_experience = int(exp_match.group(1))
-        except: pass
+    experience_match = re.search(experience_pattern, resume_summary, re.IGNORECASE)
+    if experience_match:
+        try:
+            years_experience = int(experience_match.group(1))
+        except:
+            pass
     
-    # Calculate scores
+    # Calculate match scores
     exp_match_ratio = min(1.0, years_experience / max(1, years_required)) if years_required > 0 else 0.5
-    title_words = [w for w in job_title.lower().split() if len(w) > 3]
-    title_match = sum(1 for w in title_words if w in resume_summary.lower()) / len(title_words) if title_words else 0
     
-    # Final scores
-    skill_score = min(2, skill_match * 3)
+    # Job title match score
+    title_words = [word for word in job_title.lower().split() if len(word) > 3]
+    title_matches = sum(1 for word in title_words if word in resume_summary.lower())
+    title_match = title_matches / len(title_words) if title_words else 0
+    
+    # Calculate individual scores
+    skill_score = min(2, skill_match_percentage * 3)
     exp_score = min(2, exp_match_ratio * 2)
     title_score = min(2, title_match * 2)
     
     # Extract candidate info
-    name = re.search(r'Name:\s*(.*?)(?=\n|\Z)', resume_summary)
-    name = name.group(1).strip() if name else "The candidate"
+    name_match = re.search(r'Name:\s*(.*?)(?=\n|\Z)', resume_summary)
+    name = name_match.group(1).strip() if name_match else "The candidate"
     
-    industry = re.search(r'Expected Industry:\s*(.*?)(?=\n|\Z)', resume_summary)
-    industry = industry.group(1).strip() if industry else "unspecified industry"
+    industry_match = re.search(r'Expected Industry:\s*(.*?)(?=\n|\Z)', resume_summary)
+    industry = industry_match.group(1).strip() if industry_match else "unspecified industry"
     
-    # Calculate weighted score
+    # Calculate final weighted score
     weighted_score = (skill_score * 0.5) + (exp_score * 0.3) + (title_score * 0.2)
-    fit_score = 2 if weighted_score >= 1.5 else (1 if weighted_score >= 0.8 else 0)
     
-    # Generate assessment
-    missing = [skill for skill in required_skills if skill not in skills_mentioned]
+    # Determine fit score
+    if weighted_score >= 1.5:
+        fit_score = 2  # Good fit
+    elif weighted_score >= 0.8:
+        fit_score = 1  # Potential fit
+    else:
+        fit_score = 0  # Not a fit
+    
+    # Generate assessment text
+    missing_skills = [skill for skill in required_skills if skill not in skills_mentioned]
     
     if fit_score == 2:
-        assessment = f"{fit_score}: GOOD FIT - {name} demonstrates strong alignment with the {job_title} position. Their background in {industry} appears well-suited for this role's requirements."
+        fit_assessment = f"{fit_score}: GOOD FIT - {name} demonstrates strong alignment with the {job_title} position. Their background in {industry} and professional experience appear well-suited for this role's requirements. The technical expertise matches what the position demands."
     elif fit_score == 1:
-        assessment = f"{fit_score}: POTENTIAL FIT - {name} shows potential for the {job_title} role but has gaps in certain areas. Additional training might be needed in {', '.join(missing[:2])}."
+        fit_assessment = f"{fit_score}: POTENTIAL FIT - {name} shows potential for the {job_title} role with some relevant experience, though there are gaps in certain technical areas. Their {industry} background provides partial alignment with the position requirements. Additional training might be needed in {', '.join(missing_skills[:2])} if pursuing this opportunity."
     else:
-        assessment = f"{fit_score}: NO FIT - {name}'s background shows limited alignment with this {job_title} position. Their experience and skills differ significantly from the requirements."
+        fit_assessment = f"{fit_score}: NO FIT - {name}'s current background shows limited alignment with this {job_title} position. Their experience level and technical background differ significantly from the role requirements. A position better matching their {industry} expertise might be more suitable."
     
-    return assessment, fit_score, time.time() - start
+    return fit_assessment, fit_score, time.time() - start_time
 
 def analyze_job_fit(resume_summary, job_description, models):
-    start = time.time()
+    """End-to-end job fit analysis"""
+    start_time = time.time()
     job_requirements = extract_job_requirements(job_description, models)
-    assessment, fit_score, _ = evaluate_job_fit(resume_summary, job_requirements, models)
-    return assessment, fit_score, time.time() - start
+    assessment, fit_score, execution_time = evaluate_job_fit(resume_summary, job_requirements, models)
+    return assessment, fit_score, time.time() - start_time
 
 #####################################
 # Main Function
 #####################################
 def main():
+    """Main function for the Streamlit application"""
     st.title("Resume-Job Fit Analyzer")
-    st.markdown("Upload your resume file in **.docx**, **.doc**, or **.txt** format and enter a job description to see how well you match.")
+    st.markdown("Upload your resume file in **.docx**, **.doc**, or **.txt** format and enter a job description to see how well you match with the job requirements.")
 
-    # Load models and get inputs
+    # Load models
     models = load_models()
-    uploaded_file = st.file_uploader("Upload your resume", type=["docx", "doc", "txt"])
+    
+    # User inputs
+    uploaded_file = st.file_uploader("Upload your resume (.docx, .doc, or .txt)", type=["docx", "doc", "txt"])
     job_description = st.text_area("Enter Job Description", height=200, placeholder="Paste the job description here...")
 
     # Process when button clicked
-    if uploaded_file and job_description and st.button("Analyze Job Fit"):
-        progress = st.progress(0)
-        status = st.empty()
+    if uploaded_file is not None and job_description and st.button("Analyze Job Fit"):
+        progress_bar = st.progress(0)
+        status_text = st.empty()
         
         # Step 1: Extract text
-        status.text("Step 1/3: Extracting text from resume...")
+        status_text.text("Step 1/3: Extracting text from resume...")
         resume_text = extract_text_from_file(uploaded_file)
-        progress.progress(25)
+        progress_bar.progress(25)
         
         if resume_text.startswith("Error") or resume_text == "Unsupported file type. Please upload a .docx, .doc, or .txt file.":
             st.error(resume_text)
         else:
             # Step 2: Generate summary
-            status.text("Step 2/3: Analyzing resume...")
-            summary, summary_time = summarize_resume_text(resume_text, models)
-            progress.progress(50)
+            status_text.text("Step 2/3: Analyzing resume and generating summary...")
+            summary, summarization_time = summarize_resume_text(resume_text, models)
+            progress_bar.progress(50)
+            
+            # Display summary
             st.subheader("Your Resume Summary")
             st.markdown(summary)
             
-            # Step 3: Evaluate fit
-            status.text("Step 3/3: Evaluating job fit...")
-            assessment, fit_score, eval_time = analyze_job_fit(summary, job_description, models)
-            progress.progress(100)
-            status.empty()
+            # Step 3: Generate job fit assessment
+            status_text.text("Step 3/3: Evaluating job fit (this will take a moment)...")
+            assessment, fit_score, assessment_time = analyze_job_fit(summary, job_description, models)
+            progress_bar.progress(100)
+            status_text.empty()
 
             # Display results
             st.subheader("Job Fit Assessment")
+            
+            # Display score with appropriate styling
             fit_labels = {0: "NOT FIT", 1: "POTENTIAL FIT", 2: "GOOD FIT"}
-            colors = {0: "red", 1: "orange", 2: "green"}
-            st.markdown(f"<h2 style='color: {colors[fit_score]};'>{fit_labels[fit_score]}</h2>", unsafe_allow_html=True)
+            score_colors = {0: "red", 1: "orange", 2: "green"}
+            st.markdown(f"<h2 style='color: {score_colors[fit_score]};'>{fit_labels[fit_score]}</h2>", unsafe_allow_html=True)
             st.markdown(assessment)
-            st.info(f"Analysis completed in {(summary_time + eval_time):.2f} seconds")
+            st.info(f"Analysis completed in {(summarization_time + assessment_time):.2f} seconds")
             
             # Recommendations
             st.subheader("Recommended Next Steps")
+            
             if fit_score == 2:
                 st.markdown("""
                 - Apply for this position as you appear to be a good match