Update app.py

app.py

@@ -48,6 +48,13 @@ def extract_skills_llama(text):
     except Exception as e:
         raise RuntimeError(f"Error during skill extraction: {e}")
 
+# --- Job Description Processing Function --- #
+def process_job_description(text):
+    """Extracts skills or relevant keywords from the job description."""
+    # You can customize this function as needed, for now, we'll use a basic extraction
+    # Using the Llama model or a simple regex-based approach for demonstration
+    return extract_skills_llama(text)
+
 # --- Qualification and Experience Extraction --- #
 def extract_qualifications(text):
     """Extracts qualifications from text (e.g., degrees, certifications)."""
@@ -163,13 +170,13 @@ def analyze_resume(resume_file, job_description_file):
         f"Qualifications Similarity: {qualifications_similarity:.2f}% ({categorize_similarity(qualifications_similarity)})",
         f"Experience Similarity: {experience_similarity:.2f}% ({categorize_similarity(experience_similarity)})",
         communication_response,
-        f"Sentiment: {sentiment}",
+        f"Sentiment Analysis: {sentiment}",
         f"Resume Skills: {', '.join(resume_skills)}",
         f"Job Description Skills: {', '.join(job_description_skills)}",
         f"Resume Qualifications: {', '.join(resume_qualifications)}",
         f"Job Description Qualifications: {', '.join(job_description_qualifications)}",
-        f"Resume Experience: {', '.join([f'{y} years' for y, _ in resume_experience])}",
-        f"Job Description Experience: {', '.join([f'{y} years' for y, _ in job_description_experience])}"
+        f"Resume Experience: {', '.join([str(e) for e in resume_experience])}",
+        f"Job Description Experience: {', '.join([str(e) for e in job_description_experience])}"
     )
 
 # --- Gradio Interface --- #