Refactor into helper functions

app.py

@@ -87,6 +87,37 @@ with col1:
 with col2:
     uploaded_job_description = st.file_uploader("Upload Job Description (.docx or .pdf)", type=["docx", "pdf"], key="job_description")
 
+def get_score(resume_path, job_description_path):
+    similarity_score = similarity_main(resume_path, job_description_path)
+    if isinstance(similarity_score, str) and '%' in similarity_score:
+        similarity_score = float(similarity_score.replace('%', ''))
+        
+        # Display messages based on score range
+        if similarity_score < 50:
+            st.markdown('<p style="color: red; font-weight: bold;">Low chance, skills gap identified!</p>', unsafe_allow_html=True)
+            pie_colors = ['#FF4B4B', '#E5E5E5']  
+        elif 50 <= similarity_score < 70:
+            st.markdown('<p style="color: red; font-weight: bold;">Good chance but you can improve further!</p>')
+            pie_colors = ['#FFC107', '#E5E5E5']  
+        else:
+            st.markdown('<p style="color: green; font-weight: bold;">Excellent! You can submit your CV.</p>', unsafe_allow_html=True)
+            pie_colors = ['#4CAF50', '#E5E5E5']  
+    
+    return similarity_score
+
+def display_score(similarity):
+    # Display Score as a Pie Chart
+    st.markdown(f"### Similarity Score: {int(similarity_score)}%")
+    
+    # Pie chart to show similarity
+    fig, ax = plt.subplots()
+    # ax.pie([similarity_score, 100 - similarity_score], labels=['Match', 'Difference'], autopct='%1.1f%%', startangle=140, colors=['#4B7BE5', '#E5E5E5'])
+    ax.pie([similarity_score, 100 - similarity_score], labels=['Match', 'Difference'], autopct='%1.1f%%', startangle=140, colors=pie_colors)
+        
+    ax.axis('equal')  
+    st.pyplot(fig)
+
+
 # Process if files are uploaded
 if uploaded_resume and uploaded_job_description:
     # Save files
@@ -99,32 +130,9 @@ if uploaded_resume and uploaded_job_description:
 
     if st.button("Resume-JD Matching"):
         with st.spinner("Computing Match"):
-            similarity_score = similarity_main(resume_path, job_description_path)
-            if isinstance(similarity_score, str) and '%' in similarity_score:
-                similarity_score = float(similarity_score.replace('%', ''))
-                
-                # Display messages based on score range
-                if similarity_score < 50:
-                    st.markdown('<p style="color: red; font-weight: bold;">Low chance, skills gap identified!</p>', unsafe_allow_html=True)
-                    pie_colors = ['#FF4B4B', '#E5E5E5']  
-                elif 50 <= similarity_score < 70:
-                    st.markdown('<p style="color: red; font-weight: bold;">Good chance but you can improve further!</p>')
-                    pie_colors = ['#FFC107', '#E5E5E5']  
-                else:
-                    st.markdown('<p style="color: green; font-weight: bold;">Excellent! You can submit your CV.</p>', unsafe_allow_html=True)
-                    pie_colors = ['#4CAF50', '#E5E5E5']  
-
-        # Display Score as a Pie Chart
-        st.markdown(f"### Similarity Score: {int(similarity_score)}%")
+            score = get_score(resume_path, job_description_path)
+            display_score(score)
         
-        # Pie chart to show similarity
-        fig, ax = plt.subplots()
-        # ax.pie([similarity_score, 100 - similarity_score], labels=['Match', 'Difference'], autopct='%1.1f%%', startangle=140, colors=['#4B7BE5', '#E5E5E5'])
-        ax.pie([similarity_score, 100 - similarity_score], labels=['Match', 'Difference'], autopct='%1.1f%%', startangle=140, colors=pie_colors)
-            
-        ax.axis('equal')  
-        st.pyplot(fig)
-
         #Autoscroll
         st.markdown("""
             <script>
@@ -138,8 +146,8 @@ if uploaded_resume and uploaded_job_description:
 
     if st.button("Tailor Resume"):
         with st.spinner("Generating resume..."):
-            generated_resume = generate_gemini(resume_path, job_description_path)
-            st.subheader("Generated Tailored Resume:")
+            generated_resume, new_resume_path = generate_gemini(resume_path, job_description_path)
+            st.markdown("Generated Tailored Resume:")
             # st.write(generated_resume)
 
             #Autoscroll
@@ -148,7 +156,11 @@ if uploaded_resume and uploaded_job_description:
                     window.scrollTo(0, document.body.scrollHeight);
                 </script>
             """, unsafe_allow_html=True)
-            
+
+            with st.spinner("Computing Match"):
+                score = get_score(new_resume_path, job_description_path)
+                display_score(score)
+                
             if generated_resume is not None:
                 from io import BytesIO
                 from docx import Document
@@ -166,7 +178,7 @@ if uploaded_resume and uploaded_job_description:
                     file_name="tailored_resume.docx",
                     mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
                 )
-           
+               
 
 else:
     st.warning("Please upload both the resume and job description files.")