Update app.py

app.py

@@ -66,6 +66,18 @@ def extract_experience(text):
     experience_years = [int(year[0]) for year in experience_years]
     return experience_years, job_titles
 
+# --- Summarization Function --- #
+def summarize_experience(experience_text):
+    """Summarizes the experience text using a pre-trained model."""
+    model_name = "facebook/bart-large-cnn"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+    inputs = tokenizer(experience_text, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(inputs['input_ids'], max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
 # --- Semantic Similarity Calculation --- #
 def calculate_semantic_similarity(text1, text2):
     """Calculates semantic similarity using a sentence transformer model and returns the score as a percentage."""
@@ -147,37 +159,37 @@ def analyze_resume(resume_file, job_description_file):
     resume_experience, resume_job_titles = extract_experience(resume_text)
     job_description_experience, job_description_titles = extract_experience(job_description_text)
 
+    # Summarize experiences
+    resume_experience_summary = summarize_experience(resume_text)
+    job_description_experience_summary = summarize_experience(job_description_text)
+
     # Calculate semantic similarity for different sections in percentages
     skills_similarity = calculate_semantic_similarity(' '.join(resume_skills), ' '.join(job_description_skills))
     qualifications_similarity = calculate_semantic_similarity(' '.join(resume_qualifications), ' '.join(job_description_qualifications))
     experience_similarity = calculate_semantic_similarity(' '.join([str(e) for e in resume_experience]), ' '.join([str(e) for e in job_description_experience]))
 
-    # Assuming candidate experience is extracted from the resume
-    candidate_experience = sum(resume_experience)  # Calculate total experience based on extracted years
+    # Assuming candidate experience is the total of years from the resume
+    candidate_experience = sum(resume_experience)
 
-    # Generate a communication response based on the similarity percentages
-    communication_response = communication_generator(
-        resume_skills, job_description_skills, 
-        skills_similarity, qualifications_similarity, experience_similarity,
+    # Generate communication based on analysis
+    response_message = communication_generator(
+        resume_skills,
+        job_description_skills,
+        skills_similarity,
+        qualifications_similarity,
+        experience_similarity,
         candidate_experience
     )
 
-    # Perform Sentiment Analysis
-    sentiment = sentiment_analysis(resume_text)
-
-    # Return the results including thresholds and percentage scores
-    return (
-        f"Skills Similarity: {skills_similarity:.2f}% ({categorize_similarity(skills_similarity)})",
-        f"Qualifications Similarity: {qualifications_similarity:.2f}% ({categorize_similarity(qualifications_similarity)})",
-        f"Experience Similarity: {experience_similarity:.2f}% ({categorize_similarity(experience_similarity)})",
-        communication_response,
-        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"Candidate Experience: {candidate_experience} years"
-    )
+    return {
+        "Resume Experience Summary": resume_experience_summary,
+        "Job Description Experience Summary": job_description_experience_summary,
+        "Skills Similarity": skills_similarity,
+        "Qualifications Similarity": qualifications_similarity,
+        "Experience Similarity": experience_similarity,
+        "Candidate Experience": candidate_experience,
+        "Response Message": response_message,
+    }
 
 # --- Gradio Interface --- #
 with gr.Blocks() as demo: