Update app.py

app.py

@@ -51,6 +51,37 @@ def extract_contact_info(resume_text):
     
     return name, email, contact
 
+# Function to extract relevant skills from the resume text
+def extract_relevant_skills(resume_text):
+    # Look for sections related to skills/competencies
+    skills_section_keywords = ["skills", "technical skills", "competencies", "technologies", "tools"]
+    skills = []
+    
+    for keyword in skills_section_keywords:
+        pattern = r"(?i)(%s):?\s*([^\n]+)" % re.escape(keyword)
+        matches = re.findall(pattern, resume_text)
+        for match in matches:
+            skills += match[1].split(",")  # Split skills by comma
+    
+    # Clean up and remove empty strings or leading/trailing spaces
+    skills = [skill.strip() for skill in skills if skill.strip()]
+    
+    return skills
+
+# Function to extract educational background from the resume text
+def extract_educational_background(resume_text):
+    # Look for common phrases related to education
+    education_keywords = ["bachelor", "master", "degree", "university", "college", "diploma"]
+    education = []
+    
+    for keyword in education_keywords:
+        pattern = r"(?i)(%s)[^,]*[\.,;]" % re.escape(keyword)
+        matches = re.findall(pattern, resume_text)
+        for match in matches:
+            education.append(match.strip())
+    
+    return education
+
 # Function to extract years of team leadership or management experience from the job description
 def extract_expected_years(job_description):
     # Use regex to extract any number of years mentioned in the job description for management or team leadership
@@ -82,6 +113,18 @@ def extract_direct_team_leadership_years(text):
     
     return total_years
 
+# Function to calculate match percentage based on experience
+def calculate_match_percentage(direct_team_leadership_years, direct_management_years):
+    # Give more weight to leadership and management experience
+    if direct_team_leadership_years >= 2 and direct_management_years >= 3:
+        return 80  # High match
+    elif direct_team_leadership_years >= 1 and direct_management_years >= 2:
+        return 70  # Moderate match
+    elif direct_team_leadership_years > 0 or direct_management_years > 0:
+        return 50  # Low to moderate match
+    else:
+        return 30  # Very low match
+
 # Refined Prompt Template for Gemini API (requesting detailed text about management experience)
 input_prompt = """
 Act as a sophisticated Applicant Tracking System (ATS) with expertise in evaluating resumes specifically for management and team leadership roles. Your task is to analyze the resume in relation to the job description, focusing on direct and indirect team management experience, skills, and qualifications.
@@ -100,7 +143,7 @@ Provide a detailed output that includes:
 - **Direct Management Experience (in years)**: Quantify and describe this experience with specific examples.
 - **Relevant Skills and Qualifications**: List skills and qualifications relevant to management and team leadership.
 - **Educational Background**: Provide a brief summary.
-- **Match Percentage**: Set to 70%.
+- **Match Percentage**: Calculate the match percentage (between 30%-100%) based on leadership and management experience.
 
 Input:
 - Resume Text: "{text}"
@@ -129,6 +172,10 @@ if uploaded_file and job_description:
         # Extract contact info (name, email, contact)
         name, email, contact = extract_contact_info(resume_text)
 
+        # Extract relevant skills and educational background
+        relevant_skills = extract_relevant_skills(resume_text)
+        educational_background = extract_educational_background(resume_text)
+
         # Extract years of experience for Direct Team Leadership and Management
         direct_team_leadership_years = extract_direct_team_leadership_years(resume_text)
         direct_management_years = direct_team_leadership_years  # Reuse extraction logic for simplicity
@@ -142,8 +189,8 @@ if uploaded_file and job_description:
         # Clean up the response to remove unnecessary whitespace or formatting
         response_text_clean = response_text.strip()
 
-        # Set the match percentage to 70%
-        match_percentage = 70
+        # Set the match percentage based on extracted data
+        match_percentage = calculate_match_percentage(direct_team_leadership_years, direct_management_years)
 
         # Determine the Job Description Match Score
         job_description_match_score = "High" if match_percentage >= 80 else "Moderate" if match_percentage >= 50 else "Low"
@@ -155,8 +202,8 @@ if uploaded_file and job_description:
             'Contact': [contact],
             'Direct_Team_Leadership_Experience_Years': [direct_team_leadership_years],
             'Direct_Management_Experience_Years': [direct_management_years],
-            'Relevant_Skills_and_Qualifications': ["Placeholder for skills"],  # To be updated with real data
-            'Educational_Background': ["Placeholder for education"],  # To be updated with real data
+            'Relevant_Skills_and_Qualifications': [", ".join(relevant_skills)],
+            'Educational_Background': [", ".join(educational_background)],
             'Model_Response': [response_text_clean],
             'Match_Percentage': [match_percentage],
             'Job_Description_Match_Score': [job_description_match_score]