Update .app

.app

@@ -7,17 +7,28 @@ from sklearn.metrics.pairwise import cosine_similarity
 import gradio as gr
 import os
 import subprocess
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
 
 # Download spaCy model if not already present
 try:
+    logger.info("Loading spaCy model...")
     spacy.load("en_core_web_sm")
 except OSError:
+    logger.info("Downloading spaCy model 'en_core_web_sm'...")
     subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
+    logger.info("spaCy model downloaded successfully.")
 
 class ATSValidator:
     def __init__(self):
+        logger.info("Initializing ATSValidator...")
         self.nlp = spacy.load("en_core_web_sm")
+        logger.info("spaCy model loaded.")
         self.bert_model = SentenceTransformer('all-MiniLM-L6-v2')
+        logger.info("SentenceTransformer model loaded.")
         self.section_weights = {
             'experience': 0.35,
             'education': 0.25,
@@ -25,7 +36,6 @@ class ATSValidator:
             'projects': 0.15,
             'certifications': 0.05
         }
-        # Common technical skills
         self.key_skills = {
             'python', 'javascript', 'java', 'sql', 'aws', 'docker', 'react',
             'nodejs', 'machine learning', 'data analysis', 'git', 'html', 'css'
@@ -33,11 +43,13 @@ class ATSValidator:
 
     def extract_text(self, pdf_path):
         if not os.path.exists(pdf_path):
+            logger.error(f"PDF file not found: {pdf_path}")
             return ""
         try:
             with fitz.open(pdf_path) as doc:
                 return " ".join(page.get_text("text") for page in doc)[:100000]
-        except:
+        except Exception as e:
+            logger.error(f"Error extracting text from PDF: {str(e)}")
             return ""
 
     def preprocess_text(self, text):
@@ -62,11 +74,12 @@ class ATSValidator:
             embeddings = self.bert_model.encode([resume_text[:1024], jd_text[:1024]])
             similarity = cosine_similarity([embeddings[0]], [embeddings[1]])
             return round(max(0, similarity.item() * 100), 2)
-        except:
+        except Exception as e:
+            logger.error(f"Error calculating semantic fit: {str(e)}")
             return 0
 
     def analyze(self, resume_file, job_description):
-        # Extract text from uploaded resume
+        logger.info("Starting resume analysis...")
         resume_text = self.extract_text(resume_file)
         if not resume_text:
             return "Error: Could not read resume file", "", "", ""
@@ -74,16 +87,13 @@ class ATSValidator:
         cleaned_resume = self.preprocess_text(resume_text)
         cleaned_jd = self.preprocess_text(job_description)
         
-        # Calculate scores
         semantic_score = self.calculate_semantic_fit(cleaned_resume, cleaned_jd)
         section_scores = self.analyze_sections(resume_text)
         section_score = sum(self.section_weights[sec] * score * 100 
                           for sec, score in section_scores.items())
         
-        # Final ATS score
         ats_score = np.clip(0.7 * semantic_score + 0.3 * section_score, 0, 100)
         
-        # Skills analysis
         resume_skills = self.extract_skills(cleaned_resume)
         jd_skills = self.extract_skills(cleaned_jd)
         matched_skills = resume_skills.intersection(jd_skills)
@@ -92,7 +102,6 @@ class ATSValidator:
         skills_match = f"Matched Skills: {', '.join(matched_skills) if matched_skills else 'None'}\n" \
                       f"Missing Skills: {', '.join(missing_skills) if missing_skills else 'None'}"
         
-        # Improvement suggestions
         improvements = []
         if semantic_score < 60:
             improvements.append("Increase alignment with job description keywords and phrases")
@@ -105,13 +114,14 @@ class ATSValidator:
             
         improvement_text = "\n".join(improvements) if improvements else "Looks good!"
         
-        # Score breakdown
         breakdown = f"Semantic Match: {semantic_score:.1f}%\nSection Score: {section_score:.1f}%"
         
+        logger.info("Analysis completed successfully.")
         return f"ATS Score: {ats_score:.1f}%", skills_match, improvement_text, breakdown
 
 # Gradio Interface
 def process_resume(resume_file, job_description):
+    logger.info("Processing resume via Gradio interface...")
     validator = ATSValidator()
     ats_score, skills_match, improvements, breakdown = validator.analyze(resume_file.name, job_description)
     return ats_score, skills_match, improvements, breakdown
@@ -135,4 +145,5 @@ interface = gr.Interface(
 
 # Launch the app
 if __name__ == "__main__":
+    logger.info("Launching Gradio app...")
     interface.launch()