Update app.py

app.py

@@ -51,8 +51,6 @@ def extract_skills_llama(text):
 # --- 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 --- #
@@ -91,33 +89,32 @@ def categorize_similarity(score):
         return "Low Match"
 
 # --- Communication Generation with Enhanced Response --- #
-def communication_generator(resume_skills, job_description_skills, skills_similarity, qualifications_similarity, experience_similarity, max_length=200):
-    """Generates a more detailed communication response based on similarity scores."""
-    model_name = "google/flan-t5-base"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
+def communication_generator(resume_skills, job_description_skills, skills_similarity, qualifications_similarity, experience_similarity, candidate_experience):
+    """Generates a detailed communication response based on similarity scores and additional criteria."""
     # Assess candidate fit based on similarity scores
     fit_status = "strong fit" if skills_similarity >= 80 and qualifications_similarity >= 80 and experience_similarity >= 80 else \
                  "moderate fit" if skills_similarity >= 50 else "weak fit"
     
-    # Create a detailed communication message based on match levels
+    # Build a message that includes a recommendation based on various assessments
+    if fit_status == "strong fit":
+        recommendation = "We recommend moving forward with this candidate, as they demonstrate a high level of alignment with the role requirements."
+    elif fit_status == "moderate fit":
+        recommendation = "This candidate shows potential; however, further assessment or interviews are recommended to clarify their fit for the role."
+    else:
+        recommendation = "We advise against moving forward with this candidate, as they do not meet the key technical requirements for the position."
+
     message = (
         f"After a detailed analysis of the candidate's resume, we found the following insights:\n\n"
-        f"- **Skills Match**: {skills_similarity:.2f}% ({categorize_similarity(skills_similarity)})\n"
-        f"- **Qualifications Match**: {qualifications_similarity:.2f}% ({categorize_similarity(qualifications_similarity)})\n"
-        f"- **Experience Match**: {experience_similarity:.2f}% ({categorize_similarity(experience_similarity)})\n\n"
+        f"- **Skills Match**: {skills_similarity:.2f}% (based on required technologies: {', '.join(job_description_skills)})\n"
+        f"- **Experience Match**: {experience_similarity:.2f}% (relevant experience: {candidate_experience} years)\n"
+        f"- **Qualifications Match**: {qualifications_similarity:.2f}%\n\n"
         f"The overall assessment indicates that the candidate is a {fit_status} for the role. "
-        f"Skills such as {', '.join(resume_skills)} align {categorize_similarity(skills_similarity).lower()} with the job's requirements of {', '.join(job_description_skills)}. "
-        f"In terms of qualifications and experience, the candidate shows a {categorize_similarity(qualifications_similarity).lower()} match with the role's needs. "
-        f"Based on these findings, we believe the candidate could potentially excel in the role, "
-        f"but additional evaluation or interviews are recommended for further clarification."
+        f"Their skills in {', '.join(resume_skills)} align with the job's requirements of {', '.join(job_description_skills)}. "
+        f"Based on their experience in web application development, particularly with technologies like {', '.join(resume_skills)}, they could contribute effectively to our team.\n\n"
+        f"**Recommendation**: {recommendation}\n"
     )
 
-    inputs = tokenizer(message, return_tensors="pt", padding=True, truncation=True)
-    response = model.generate(**inputs, max_length=max_length, num_beams=4, early_stopping=True)
-
-    return tokenizer.decode(response[0], skip_special_tokens=True)
+    return message
 
 # --- Sentiment Analysis --- #
 def sentiment_analysis(text):
@@ -155,10 +152,14 @@ def analyze_resume(resume_file, job_description_file):
     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
+
     # Generate a communication response based on the similarity percentages
     communication_response = communication_generator(
         resume_skills, job_description_skills, 
-        skills_similarity, qualifications_similarity, experience_similarity
+        skills_similarity, qualifications_similarity, experience_similarity,
+        candidate_experience
     )
 
     # Perform Sentiment Analysis
@@ -175,34 +176,19 @@ def analyze_resume(resume_file, job_description_file):
         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([str(e) for e in resume_experience])}",
-        f"Job Description Experience: {', '.join([str(e) for e in job_description_experience])}"
+        f"Candidate Experience: {candidate_experience} years"
     )
 
 # --- Gradio Interface --- #
-def main():
-    """Runs the Gradio application for resume analysis."""
-    interface = gr.Interface(
-        fn=analyze_resume,
-        inputs=[gr.File(label="Upload Resume (PDF/TXT)"), gr.File(label="Upload Job Description (PDF/TXT)")],
-        outputs=[
-            gr.Textbox(label="Skills Similarity"),
-            gr.Textbox(label="Qualifications Similarity"),
-            gr.Textbox(label="Experience Similarity"),
-            gr.Textbox(label="Communication Response"),
-            gr.Textbox(label="Sentiment Analysis"),
-            gr.Textbox(label="Resume Skills"),
-            gr.Textbox(label="Job Description Skills"),
-            gr.Textbox(label="Resume Qualifications"),
-            gr.Textbox(label="Job Description Qualifications"),
-            gr.Textbox(label="Resume Experience"),
-            gr.Textbox(label="Job Description Experience")
-        ],
-        title="Resume and Job Description Analysis",
-        description="Analyze a resume against a job description to evaluate skills, qualifications, experience, and generate communication insights."
-    )
-
-    interface.launch()
-
-if __name__ == "__main__":
-    main()
+with gr.Blocks() as demo:
+    gr.Markdown("## Resume and Job Description Analyzer")
+    with gr.Row():
+        resume_file_input = gr.File(label="Upload Resume (PDF/TXT)", type="file")
+        job_description_file_input = gr.File(label="Upload Job Description (PDF/TXT)", type="file")
+    analyze_button = gr.Button("Analyze")
+    results_output = gr.Output()
+
+    analyze_button.click(analyze_resume, inputs=[resume_file_input, job_description_file_input], outputs=results_output)
+
+# Launch Gradio Interface
+demo.launch()