Update app.py

app.py

@@ -133,78 +133,76 @@ def analyze_sentiment(text):
 
 # --- Updated Resume Analysis Function --- #
 def analyze_resume(resume_file, job_description_file):
-    """Analyzes the resume and job description for skills, qualifications, and experience."""
     # Load and preprocess resume and job description
     resume_text = extract_text_from_file(resume_file)
     job_description_text = extract_text_from_file(job_description_file)
 
-    # Extract skills, qualifications, and experience
+    # Extract skills, qualifications, and experience from the resume
     resume_skills = extract_skills_llama(resume_text)
-    job_description_skills = process_job_description(job_description_text)
     resume_qualifications = extract_qualifications(resume_text)
+    resume_experience, _ = extract_experience(resume_text)
+    total_experience = sum(resume_experience)  # Assuming this returns a list of experiences
+
+    # Extract required skills, qualifications, and experience from the job description
+    job_description_skills = process_job_description(job_description_text)
     job_description_qualifications = extract_qualifications(job_description_text)
-    resume_experience, resume_job_titles = extract_experience(resume_text)
-    job_description_experience, job_description_titles = extract_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 the total of years from the resume
-    candidate_experience = sum(resume_experience)
-
-    # Generate communication based on analysis
-    response_message = communication_generator(
-        resume_skills,
-        job_description_skills,
-        skills_similarity,
-        qualifications_similarity,
-        experience_similarity,
-        candidate_experience
+    job_description_experience, _ = extract_experience(job_description_text)
+    required_experience = sum(job_description_experience)  # Assuming total years required
+
+    # Calculate similarity scores
+    skills_similarity = len(set(resume_skills).intersection(set(job_description_skills))) / len(job_description_skills) * 100
+    qualifications_similarity = len(set(resume_qualifications).intersection(set(job_description_qualifications))) / len(job_description_qualifications) * 100
+    experience_similarity = 1.0 if total_experience >= required_experience else 0.0
+
+    # Fit assessment logic
+    fit_score = 0
+    if total_experience >= required_experience:
+        fit_score += 1
+    if skills_similarity > 50:  # Define a threshold for skills match
+        fit_score += 1
+    if qualifications_similarity > 50:  # Define a threshold for qualifications match
+        fit_score += 1
+
+    # Determine fit
+    if fit_score == 3:
+        fit_assessment = "Strong fit"
+    elif fit_score == 2:
+        fit_assessment = "Moderate fit"
+    else:
+        fit_assessment = "Not a fit"
+
+    # Prepare output message
+    response_message = (
+        f"Skills Similarity: {skills_similarity:.2f}%\n"
+        f"Qualifications Similarity: {qualifications_similarity:.2f}%\n"
+        f"Experience Similarity: {experience_similarity * 100:.2f}%\n"
+        f"Candidate Experience: {total_experience} years\n"
+        f"Sentiment: Neutral\n"
+        f"Response Message: After a detailed analysis of the candidate's resume, we found the following insights:\n\n"
+        f"Skills Match: {skills_similarity:.2f}% (based on required technologies: {', '.join(job_description_skills)})\n"
+        f"Qualifications Match: {qualifications_similarity:.2f}%\n"
+        f"Experience Match: {experience_similarity * 100:.2f}% (relevant experience: {total_experience} years)\n"
+        f"The overall assessment indicates that the candidate is a {fit_assessment} for the role."
     )
 
-    # Analyze sentiment for the resume
-    sentiment = analyze_sentiment(resume_text)
+    return response_message
 
-    return {
-        "skills_similarity": skills_similarity,
-        "qualifications_similarity": qualifications_similarity,
-        "experience_similarity": experience_similarity,
-        "response_message": response_message,
-        "sentiment": sentiment,
-        "candidate_experience": candidate_experience
-    }
-
-# --- Gradio Interface --- #
-# --- Gradio Interface --- #
 def run_gradio_interface():
-    """Runs the Gradio app interface."""
     with gr.Blocks() as demo:
-        gr.Markdown("# Resume Analyzer")
-        with gr.Row():
-            resume_file_input = gr.File(label="Upload Resume (PDF/TXT)")
-            job_description_file_input = gr.File(label="Upload Job Description (PDF/TXT)")
-            analyze_button = gr.Button("Analyze Resume")
-        
-        # Change output_display to gr.Markdown for displaying the analysis results
-        output_display = gr.Markdown("### Analysis Results will be displayed here")
-
-        def analyze_callback(resume_file, job_description_file):
-            analysis_results = analyze_resume(resume_file, job_description_file)
-            return (
-                f"**Skills Similarity**: {analysis_results['skills_similarity']:.2f}%\n"
-                f"**Qualifications Similarity**: {analysis_results['qualifications_similarity']:.2f}%\n"
-                f"**Experience Similarity**: {analysis_results['experience_similarity']:.2f}%\n"
-                f"**Candidate Experience**: {analysis_results['candidate_experience']} years\n"
-                f"**Sentiment**: {analysis_results['sentiment']}\n"
-                f"**Response Message**:\n{analysis_results['response_message']}"
-            )
-
-        analyze_button.click(analyze_callback, inputs=[resume_file_input, job_description_file_input], outputs=output_display)
+        gr.Markdown("## Resume and Job Description Analyzer")
+        resume_file = gr.File(label="Upload Resume")
+        job_description_file = gr.File(label="Upload Job Description")
+        output_display = gr.Textbox(label="Analysis Output", interactive=False)
 
-    demo.launch()
+        def analyze(resume, job_desc):
+            if resume and job_desc:
+                return analyze_resume(resume, job_desc)
+            return "Please upload both files."
 
+        analyze_button = gr.Button("Analyze")
+        analyze_button.click(analyze, inputs=[resume_file, job_description_file], outputs=output_display)
+
+    demo.launch()
 
 if __name__ == "__main__":
-    run_gradio_interface()
+    run_gradio_interface()