Update app.py

app.py

@@ -7,6 +7,7 @@ import re
 import google.generativeai as genai
 import pandas as pd
 import time
+import concurrent.futures
 
 # Load pre-trained embedding model for basic analysis
 sentence_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
@@ -151,6 +152,44 @@ def calculate_overall_match(leadership_years, management_years, skills, required
     overall_match = (leadership_score * leadership_weight) + (management_score * management_weight) + (skill_score * skills_weight)
     return round(overall_match, 2)
 
+def process_resume(resume, job_desc, progress_callback):
+    resume_text = extract_text_from_file(resume.name)
+    
+    if not resume_text.strip():
+        return {
+            "Resume": resume.name,
+            "Candidate Name": "N/A",
+            "Email": "N/A",
+            "Contact": "N/A",
+            "Overall Match Percentage": 0.0,
+            "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
+        overall_match = calculate_overall_match(leadership_years, management_years, skills, required_skills)
+        # Extract candidate details
+        name, email, contact = extract_candidate_details(gemini_analysis)
+    except Exception as e:
+        gemini_analysis = f"Gemini analysis failed: {str(e)}"
+        name, email, contact = "N/A", "N/A", "N/A"
+        overall_match = 0.0
+
+    progress_callback(1)  # Update progress for this resume
+
+    return {
+        "Resume": resume.name,
+        "Candidate Name": name,
+        "Email": email,
+        "Contact": contact,
+        "Overall Match Percentage": f"{overall_match}%",
+        "Gemini Analysis": gemini_analysis
+    }
+
 def process_resumes(job_desc_file, resumes):
     if not job_desc_file or not resumes:
         return "Please upload a job description and resumes for analysis."
@@ -162,43 +201,17 @@ def process_resumes(job_desc_file, resumes):
     job_desc = extract_text_from_file(job_desc_file)
 
     results = []
-    for resume in resumes:
-        resume_text = extract_text_from_file(resume.name)
-
-        if not resume_text.strip():
-            results.append({
-                "Resume": resume.name,
-                "Candidate Name": "N/A",
-                "Email": "N/A",
-                "Contact": "N/A",
-                "Overall Match Percentage": 0.0,
-                "Gemini Analysis": "Failed to extract text from resume."
-            })
-            continue
-
-        # 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
-            overall_match = calculate_overall_match(leadership_years, management_years, skills, required_skills)
-            # Extract candidate details
-            name, email, contact = extract_candidate_details(gemini_analysis)
-        except Exception as e:
-            gemini_analysis = f"Gemini analysis failed: {str(e)}"
-            name, email, contact = "N/A", "N/A", "N/A"
-            overall_match = 0.0
-
-        results.append({
-            "Resume": resume.name,
-            "Candidate Name": name,
-            "Email": email,
-            "Contact": contact,
-            "Overall Match Percentage": f"{overall_match}%",
-            "Gemini Analysis": gemini_analysis
-        })
+    total_resumes = len(resumes)
+
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        future_to_resume = {
+            executor.submit(process_resume, resume, job_desc, lambda p: None): resume for resume in resumes
+        }
 
+        for future in concurrent.futures.as_completed(future_to_resume):
+            result = future.result()
+            results.append(result)
+    
     # Create a pandas DataFrame for better formatting and downloadable output
     df = pd.DataFrame(results)