Update app.py

app.py

@@ -126,4 +126,233 @@ def get_text_embeddings(text):
     else: return np.mean([token.vector for token in doc if token.has_vector], axis=0) if [token.vector for token in doc if token.has_vector] else np.zeros(nlp.vocab.vectors.shape[1])
 
 def calculate_cosine_similarity(vec1, vec2):
-    if np
+    # This check prevents errors if one of the vectors is all zeros.
+    if np.all(vec1 == 0) or np.all(vec2 == 0):
+        return 0.0
+    vec1 = vec1.reshape(1, -1)
+    vec2 = vec2.reshape(1, -1)
+    return cosine_similarity(vec1, vec2)[0][0]
+
+# --- Main Processing Pipeline ---
+def analyze_document(doc_text):
+    doc_spacy = nlp(doc_text)
+    cleaned_text = preprocess_text(doc_text)
+    extracted_skills, general_entities = extract_skills(doc_spacy, skill_keywords=predefined_skills_list)
+    years_exp, education_level = extract_experience_and_education(doc_text)
+    text_embedding = get_text_embeddings(cleaned_text)
+    return {
+        "raw_text": doc_text, "cleaned_text": cleaned_text, "spacy_doc": doc_spacy,
+        "extracted_skills": extracted_skills, "general_entities": general_entities,
+        "years_experience": years_exp, "education_level": education_level,
+        "text_embedding": text_embedding
+    }
+
+# --- Matching and Scoring Logic ---
+def calculate_match_scores(cv_data, jd_data):
+    results = {}
+    overall_similarity = calculate_cosine_similarity(cv_data["text_embedding"], jd_data["text_embedding"])
+    results["overall_match_score"] = round(overall_similarity * 100, 2)
+    cv_skills = set(cv_data["extracted_skills"])
+    jd_skills = set(jd_data["extracted_skills"])
+    matched_skills = list(cv_skills.intersection(jd_skills))
+    missing_skills = list(jd_skills.difference(cv_skills))
+    extra_skills_in_cv = list(cv_skills.difference(jd_skills))
+    results["matched_skills"] = matched_skills
+    results["missing_skills"] = missing_skills
+    results["extra_skills_in_cv"] = extra_skills_in_cv
+    if jd_skills: skill_match_percentage = len(matched_skills) / len(jd_skills) * 100
+    else: skill_match_percentage = 0.0
+    results["skill_match_percentage"] = round(skill_match_percentage, 2)
+    corpus = [cv_data["cleaned_text"], jd_data["cleaned_text"]]
+    tfidf_vectorizer = TfidfVectorizer(max_features=100)
+    tfidf_matrix = tfidf_vectorizer.fit_transform(corpus)
+    feature_names = tfidf_vectorizer.get_feature_names_out()
+    cv_tfidf_scores = {feature_names[i]: tfidf_matrix[0, i] for i in tfidf_matrix[0].nonzero()[1]}
+    jd_tfidf_scores = {feature_names[i]: tfidf_matrix[1, i] for i in tfidf_matrix[1].nonzero()[1]}
+    top_cv_keywords = sorted(cv_tfidf_scores.items(), key=lambda x: x[1], reverse=True)[:15]
+    top_jd_keywords = sorted(jd_tfidf_scores.items(), key=lambda x: x[1], reverse=True)[:15]
+    results["top_cv_keywords"] = [k for k,v in top_cv_keywords]
+    results["top_jd_keywords"] = [k for k,v in top_jd_keywords]
+    common_keywords = set(results["top_cv_keywords"]).intersection(set(results["top_jd_keywords"]))
+    results["common_keywords"] = list(common_keywords)
+    cv_exp_years = cv_data["years_experience"]
+    jd_exp_years = jd_data["years_experience"]
+    results["cv_years_experience"] = cv_exp_years
+    results["jd_years_experience"] = jd_exp_years
+    exp_status = "Not specified by Job"
+    if jd_exp_years > 0:
+        if cv_exp_years >= jd_exp_years: exp_status = "Meets or Exceeds Requirement"
+        else: exp_status = f"Below Requirement (Needs {jd_exp_years - cv_exp_years} more years)"
+    results["experience_match_status"] = exp_status
+    cv_edu = cv_data["education_level"]
+    jd_edu = jd_data["education_level"]
+    results["cv_education_level"] = cv_edu
+    results["jd_education_level"] = jd_edu
+    edu_match_status = "Not Specified by Job"
+    if jd_edu != "Not Specified":
+        edu_order = {"Associate's": 1, "Bachelor's": 2, "Master's": 3, "Ph.D.": 4}
+        if edu_order.get(cv_edu, 0) >= edu_order.get(jd_edu, 0): edu_match_status = "Meets or Exceeds Requirement"
+        else: edu_match_status = "Below Requirement"
+    results["education_match_status"] = edu_match_status
+    return results
+
+# --- Overall Analysis Orchestrator ---
+def perform_cv_job_analysis(cv_text, job_desc_text):
+    cv_analysis_data = analyze_document(cv_text)
+    job_desc_analysis_data = analyze_document(job_desc_text)
+    match_results = calculate_match_scores(cv_analysis_data, job_desc_analysis_data)
+    return match_results
+
+# --- Visualization Functions ---
+def create_overall_match_plot(score):
+    fig, ax = plt.subplots(figsize=(6, 2))
+    sns.set_style("whitegrid")
+    ax.barh(["Overall Match"], [score], color='skyblue')
+    ax.set_xlim(0, 100)
+    ax.text(score + 2, 0, f'{score}%', va='center', color='black', fontsize=12)
+    ax.set_title("Overall CV-Job Description Match Score", fontsize=14)
+    ax.set_xlabel("Match Percentage", fontsize=12)
+    ax.get_yaxis().set_visible(False)
+    plt.tight_layout()
+    return fig
+
+def create_skill_match_plot(matched_skills, missing_skills):
+    labels = ['Matched Skills', 'Missing Skills']
+    sizes = [len(matched_skills), len(missing_skills)]
+    colors = ['#66b3ff', '#ff9999']
+    explode = (0.05, 0.05) if sizes[0] > 0 and sizes[1] > 0 else (0,0)
+    if sum(sizes) == 0: return None
+    fig, ax = plt.subplots(figsize=(7, 7))
+    ax.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90, textprops={'fontsize': 12})
+    ax.axis('equal')
+    ax.set_title("Skill Match Breakdown", fontsize=14)
+    plt.tight_layout()
+    return fig
+
+def create_top_keywords_plot(cv_keywords, jd_keywords):
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6))
+    sns.set_style("whitegrid")
+    cv_df = pd.DataFrame(Counter(cv_keywords).most_common(10), columns=['Keyword', 'Count'])
+    if not cv_df.empty:
+        sns.barplot(x='Count', y='Keyword', data=cv_df, ax=axes[0], palette='viridis')
+        axes[0].set_title('Top Keywords in CV', fontsize=14)
+        axes[0].set_xlabel('Frequency/Importance', fontsize=12)
+        axes[0].set_ylabel('')
+    jd_df = pd.DataFrame(Counter(jd_keywords).most_common(10), columns=['Keyword', 'Count'])
+    if not jd_df.empty:
+        sns.barplot(x='Count', y='Keyword', data=jd_df, ax=axes[1], palette='plasma')
+        axes[1].set_title('Top Keywords in Job Description', fontsize=14)
+        axes[1].set_xlabel('Frequency/Importance', fontsize=12)
+        axes[1].set_ylabel('')
+    plt.tight_layout()
+    return fig
+
+# --- Main Gradio Interface Function ---
+def analyze_cv_match(cv_file_obj, cv_text_input, jd_text_input):
+    cv_content = ""
+    if cv_file_obj is not None:
+        cv_content = get_file_content(cv_file_obj)
+    elif cv_text_input:
+        cv_content = cv_text_input
+    if not cv_content:
+        return (f"<h4><p style='color:red;'>🚨 Error: Please upload a CV file or paste your CV text.</p></h4>",
+                None, None, None, "Analysis Failed")
+    if not jd_text_input:
+        return (f"<h4><p style='color:red;'>🚨 Error: Please paste the Job Description text.</p></h4>",
+                None, None, None, "Analysis Failed")
+    try:
+        analysis_results = perform_cv_job_analysis(cv_content, jd_text_input)
+        
+        # Using helper variables for clarity and to prevent syntax issues in f-strings
+        matched_skills_str = ', '.join(analysis_results['matched_skills']) if analysis_results['matched_skills'] else 'None found matching job description.'
+        missing_skills_str = ', '.join(analysis_results['missing_skills']) if analysis_results['missing_skills'] else '🥳 None! Your CV has all specified skills.'
+        extra_skills_str = ', '.join(analysis_results['extra_skills_in_cv']) if analysis_results['extra_skills_in_cv'] else 'None. (This is often fine, showing broader capability.)'
+        common_keywords_str = ', '.join(analysis_results['common_keywords']) if analysis_results['common_keywords'] else 'No significant common keywords beyond skills.'
+        cv_keywords_str = ', '.join(analysis_results['top_cv_keywords']) if analysis_results['top_cv_keywords'] else 'N/A'
+        jd_keywords_str = ', '.join(analysis_results['top_jd_keywords']) if analysis_results['top_jd_keywords'] else 'N/A'
+
+        # Build the HTML string piece by piece
+        html_output = f"""
+        <h2 style='text-align: center;'>💡 Analysis Results Summary 💡</h2>
+        <div style='display: flex; justify-content: space-around; flex-wrap: wrap; text-align: center; margin-bottom: 20px;'>
+            <div style='background-color: #e0f7fa; padding: 15px; border-radius: 8px; margin: 5px; min-width: 200px; box-shadow: 2px 2px 5px rgba(0,0,0,0.1);'>
+                <h3>Overall Match Score</h3>
+                <h1 style='color: #007bb6;'>{analysis_results['overall_match_score']}%</h1>
+            </div>
+            <div style='background-color: #e8f5e9; padding: 15px; border-radius: 8px; margin: 5px; min-width: 200px; box-shadow: 2px 2px 5px rgba(0,0,0,0.1);'>
+                <h3>Skill Match</h3>
+                <h1 style='color: #43a047;'>{analysis_results['skill_match_percentage']}%</h1>
+            </div>
+            <div style='background-color: #fff3e0; padding: 15px; border-radius: 8px; margin: 5px; min-width: 200px; box-shadow: 2px 2px 5px rgba(0,0,0,0.1);'>
+                <h3>Experience Match</h3>
+                <h1 style='color: #fb8c00;'>{analysis_results['experience_match_status']}</h1>
+            </div>
+        </div>
+        <hr style='border-top: 2px solid #bbb; margin: 20px 0;'/>
+        <h2 style='text-align: center;'>📝 Detailed Breakdown</h2>
+        <h4>Skills Analysis</h4>
+        <p><strong>✅ Matched Skills:</strong> {matched_skills_str}</p>
+        <p><strong>❌ Missing Skills (from Job Description):</strong> {missing_skills_str}</p>
+        <p><strong>💡 Extra Skills in CV (not in Job Description):</strong> {extra_skills_str}</p>
+        <h4>Keyword Relevance (Top TF-IDF Terms)</h4>
+        <p><strong>🤝 Top Common Keywords:</strong> {common_keywords_str}</p>
+        <p><strong>🔍 Top Keywords in Your CV:</strong> {cv_keywords_str}</p>
+        <p><strong>🎯 Top Keywords in Job Description:</strong> {jd_keywords_str}</p>
+        <h4>Experience & Education Comparison</h4>
+        <p><strong>👤 Your CV's Experience:</strong> <code>{analysis_results['cv_years_experience']}</code> years</p>
+        <p><strong>💼 Job's Required Experience:</strong> <code>{analysis_results['jd_years_experience']}</code> years</p>
+        <p style='color:green;'><strong>Status:</strong> {analysis_results['experience_match_status']}</p>
+        <p><strong>🎓 Your CV's Education:</strong> <code>{analysis_results['cv_education_level']}</code></p>
+        <p><strong>📚 Job's Required Education:</strong> <code>{analysis_results['jd_education_level']}</code></p>
+        <p style='color:green;'><strong>Status:</strong> {analysis_results['education_match_status']}</p>
+        """
+
+        # Generate plots
+        overall_plot = create_overall_match_plot(analysis_results['overall_match_score'])
+        skill_plot = create_skill_match_plot(analysis_results['matched_skills'], analysis_results['missing_skills'])
+        keywords_plot = create_top_keywords_plot(analysis_results['top_cv_keywords'], analysis_results['top_jd_keywords'])
+        return html_output, overall_plot, skill_plot, keywords_plot, "Analysis Complete!"
+    except Exception as e:
+        import traceback
+        error_traceback = traceback.format_exc()
+        return (f"<h4><p style='color:red;'>An unexpected error occurred during analysis: {e}</p></h4>"
+                f"<details><summary>Click for details</summary><pre>{error_traceback}</pre></details>",
+                None, None, None, "Analysis Failed")
+
+# --- Gradio Interface Definition ---
+with gr.Blocks(theme=gr.themes.Soft(), title="CV-Job Match Analyzer") as demo:
+    
+    # Cleaner fix: Add CSS padding to the top of the app to push it down
+    gr.HTML("<style>#root{padding-top: 50px !important;}</style>")
+    
+    gr.Markdown(
+        """
+        # 👨‍💼 CV-Job Match Analyzer 📈
+        Welcome! This tool helps you understand how well a CV matches a job description.
+        Upload a CV (PDF, DOCX, TXT) and paste the job description text to get an instant analysis.
+        """
+    )
+    with gr.Row(): 
+        with gr.Column(scale=1, min_width=400): 
+            gr.Markdown("## **1. Your CV**") 
+            cv_file_obj = gr.File(label="Upload CV (PDF, DOCX, TXT)", file_types=[".pdf", ".docx", ".txt"])
+            cv_text_input = gr.Textbox(label="Or paste CV text here (overrides file upload)", lines=10, placeholder="Paste your CV content here...")
+            gr.Markdown("## **2. Job Description**") 
+            jd_text_input = gr.Textbox(label="Paste the Job Description text here", lines=10, placeholder="Paste the job description content here...")
+            with gr.Row():
+                analyze_button = gr.Button("✨ Analyze CV Match ✨", variant="primary", scale=1)
+                clear_button = gr.ClearButton([cv_file_obj, cv_text_input, jd_text_input], scale=1)
+        with gr.Column(scale=2, min_width=600):
+            output_html = gr.HTML(label="Analysis Report") 
+            gr.Markdown("## **📊 Visual Insights**") 
+            output_overall_plot = gr.Plot(label="Overall Match Score") 
+            output_skill_plot = gr.Plot(label="Skill Match Breakdown")
+            output_keywords_plot = gr.Plot(label="Top Keywords")
+
+    analyze_button.click(
+        fn=analyze_cv_match,
+        inputs=[cv_file_obj, cv_text_input, jd_text_input],
+        outputs=[output_html, output_overall_plot, output_skill_plot, output_keywords_plot, gr.State(value="")],
+    )
+
+demo.launch()