Update app.py

app.py

@@ -8,6 +8,7 @@ import re
 import google.generativeai as genai
 import time
 import concurrent.futures
+from fuzzywuzzy import fuzz
 
 # Load pre-trained embedding model for basic analysis
 sentence_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
@@ -67,6 +68,7 @@ required_skills = [
 ]
 
 # Helper Functions
+
 def extract_text_from_file(file_path):
     ext = os.path.splitext(file_path)[1].lower()
     if ext == ".txt":
@@ -102,7 +104,6 @@ def analyze_with_gemini(resume_text, job_desc):
     return response.text.strip()
 
 def extract_management_details(gemini_response):
-    # Regular expressions for extracting leadership and management details
     leadership_exp_pattern = r"Team Leadership Experience \(years\):\s*(\d+)"
     management_exp_pattern = r"Management Experience \(years\):\s*(\d+)"
     management_skills_pattern = r"Management Skills\s*[:\-]?\s*(.*?)(?=\n|$)"
@@ -118,7 +119,6 @@ def extract_management_details(gemini_response):
     return leadership_years, management_years, skills
 
 def extract_candidate_details(gemini_response):
-    # Regular expressions for extracting candidate details
     name_pattern = r"Candidate Name\s*[:\-]?\s*(.*?)(?=\n|$)"
     email_pattern = r"Email Address\s*[:\-]?\s*(.*?)(?=\n|$)"
     contact_pattern = r"Contact Number\s*[:\-]?\s*(.*?)(?=\n|$)"
@@ -134,7 +134,6 @@ def extract_candidate_details(gemini_response):
     return name, email, contact
 
 def calculate_role_score(role_keywords):
-    # Score based on the seniority of the leadership role
     seniority_score = 0
     role_hierarchy = {
         "CEO": 5,
@@ -147,32 +146,27 @@ def calculate_role_score(role_keywords):
     }
 
     for keyword, score in role_hierarchy.items():
-        if keyword.lower() in role_keywords.lower():
-            seniority_score = max(seniority_score, score)  # Highest score from role hierarchy
+        if fuzz.partial_ratio(keyword.lower(), role_keywords.lower()) > 80:
+            seniority_score = max(seniority_score, score)
     
     return seniority_score
 
 def calculate_advanced_match(leadership_years, management_years, skills, required_skills, role_keywords, max_leadership_exp=10, max_management_exp=10):
-    # Assign weights for leadership, management, and skills
-    leadership_weight = 0.35  # 35% weight to leadership
-    management_weight = 0.35  # 35% weight to management
-    skills_weight = 0.2      # 20% weight to skills
-    role_weight = 0.1        # 10% weight to role seniority
+    leadership_weight = 0.35
+    management_weight = 0.35
+    skills_weight = 0.2
+    role_weight = 0.1
 
-    # Normalize years of experience to a 100% scale
     leadership_score = min(leadership_years / max_leadership_exp, 1.0) * 100
     management_score = min(management_years / max_management_exp, 1.0) * 100
 
-    # Adjust for role seniority
-    role_score = calculate_role_score(role_keywords)  # Role score based on seniority of role
-    role_score = role_score * 100  # Normalize role score to 100 scale
+    role_score = calculate_role_score(role_keywords)
+    role_score = role_score * 100
 
-    # Calculate skill match: percentage of required skills found in the resume
-    skills_matched = sum(1 for skill in required_skills if skill.lower() in skills.lower())
+    skills_matched = sum(1 for skill in required_skills if fuzz.partial_ratio(skill.lower(), skills.lower()) > 80)
     total_skills = len(required_skills)
     skill_match_score = (skills_matched / total_skills) * 100
 
-    # Calculate the overall match score
     overall_match = (leadership_score * leadership_weight) + \
                     (management_score * management_weight) + \
                     (skill_match_score * skills_weight) + \
@@ -192,15 +186,11 @@ def process_resume(resume, job_desc, progress_callback):
             "Gemini Analysis": "Failed to extract text from resume."
         }
     
-    # Detailed analysis with Gemini API
     try:
         gemini_analysis = analyze_with_gemini(resume_text, job_desc)
-        # Extract leadership and management details
         leadership_years, management_years, skills = extract_management_details(gemini_analysis)
-        # Calculate overall match percentage using enhanced calculation
         role_keywords = gemini_analysis.lower()
         overall_match = calculate_advanced_match(leadership_years, management_years, skills, required_skills, role_keywords)
-        # Extract candidate details
         name, email, contact = extract_candidate_details(gemini_analysis)
     except Exception as e:
         gemini_analysis = f"Gemini analysis failed: {str(e)}"
@@ -218,16 +208,13 @@ def process_resume(resume, job_desc, progress_callback):
         "Gemini Analysis": gemini_analysis
     }
 
-# Main Gradio UI
 def analyze_resumes(resumes, job_desc):
     progress = gr.Progress()
     results = []
     
-    # Check for maximum number of resumes
     if len(resumes) > MAX_RESUMES:
         return "Error: Cannot upload more than 10 resumes."
     
-    # Process resumes concurrently
     with concurrent.futures.ThreadPoolExecutor() as executor:
         futures = []
         for resume in resumes:
@@ -239,7 +226,7 @@ def analyze_resumes(resumes, job_desc):
     resume_count_message = f"{len(resumes)} resume(s) uploaded."
     return pd.DataFrame(results), resume_count_message
 
-# Gradio Interface
+# Gradio Interface with Submit Button and Progress Bar
 iface = gr.Interface(
     fn=analyze_resumes,
     inputs=[
@@ -247,7 +234,8 @@ iface = gr.Interface(
         gr.Textbox(label="Job Description", lines=5)
     ],
     outputs=["dataframe", "text"],
-    live=True
+    live=False,  # Disable live updating
+    allow_flagging="never"  # Optional: Disables the flagging feature
 )
 
 iface.launch()