Update app.py

app.py

@@ -671,91 +671,93 @@ def analyze_job_fit(resume_summary, job_description, models):
 models = load_models()
 
 #####################################
-# Main Streamlit Interface
+# Main Function
 #####################################
-st.title("Resume-Job Fit Analyzer")
-st.markdown(
+def main():
+    """Main function for the Streamlit application"""
+    st.title("Resume-Job Fit Analyzer")
+    st.markdown(
+        """
+    Upload your resume file in **.docx**, **.doc**, or **.txt** format and enter a job description to see how well you match with the job requirements.
     """
-Upload your resume file in **.docx**, **.doc**, or **.txt** format and enter a job description to see how well you match with the job requirements.
-"""
-)
+    )
 
-# Resume upload
-uploaded_file = st.file_uploader("Upload your resume (.docx, .doc, or .txt)", type=["docx", "doc", "txt"])
+    # Resume upload
+    uploaded_file = st.file_uploader("Upload your resume (.docx, .doc, or .txt)", type=["docx", "doc", "txt"])
 
-# Job description input
-job_description = st.text_area("Enter Job Description", height=200, placeholder="Paste the job description here...")
+    # Job description input
+    job_description = st.text_area("Enter Job Description", height=200, placeholder="Paste the job description here...")
 
-# Process button with optimized flow
-if uploaded_file is not None and job_description and st.button("Analyze Job Fit"):
-    # Create a placeholder for the progress bar
-    progress_bar = st.progress(0)
-    status_text = st.empty()
-    
-    # Step 1: Extract text
-    status_text.text("Step 1/3: Extracting text from resume...")
-    resume_text = extract_text_from_file(uploaded_file)
-    progress_bar.progress(25)
-    
-    if resume_text.startswith("Error") or resume_text == "Unsupported file type. Please upload a .docx, .doc, or .txt file.":
-        st.error(resume_text)
-    else:
-        # Step 2: Generate summary
-        status_text.text("Step 2/3: Analyzing resume and generating summary...")
-        summary, summarization_time = summarize_resume_text(resume_text, models)
-        progress_bar.progress(50)
+    # Process button with optimized flow
+    if uploaded_file is not None and job_description and st.button("Analyze Job Fit"):
+        # Create a placeholder for the progress bar
+        progress_bar = st.progress(0)
+        status_text = st.empty()
         
-        # Display summary
-        st.subheader("Your Resume Summary")
-        st.markdown(summary)
+        # Step 1: Extract text
+        status_text.text("Step 1/3: Extracting text from resume...")
+        resume_text = extract_text_from_file(uploaded_file)
+        progress_bar.progress(25)
         
-        # Step 3: Generate job fit assessment
-        status_text.text("Step 3/3: Evaluating job fit (this will take a moment)...")
-        assessment, fit_score, assessment_time = analyze_job_fit(summary, job_description, models)
-        progress_bar.progress(100)
+        if resume_text.startswith("Error") or resume_text == "Unsupported file type. Please upload a .docx, .doc, or .txt file.":
+            st.error(resume_text)
+        else:
+            # Step 2: Generate summary
+            status_text.text("Step 2/3: Analyzing resume and generating summary...")
+            summary, summarization_time = summarize_resume_text(resume_text, models)
+            progress_bar.progress(50)
+            
+            # Display summary
+            st.subheader("Your Resume Summary")
+            st.markdown(summary)
+            
+            # Step 3: Generate job fit assessment
+            status_text.text("Step 3/3: Evaluating job fit (this will take a moment)...")
+            assessment, fit_score, assessment_time = analyze_job_fit(summary, job_description, models)
+            progress_bar.progress(100)
 
-        # Clear status messages
-        status_text.empty()
+            # Clear status messages
+            status_text.empty()
 
-        # Display job fit results
-        st.subheader("Job Fit Assessment")
+            # Display job fit results
+            st.subheader("Job Fit Assessment")
 
-        # Display fit score with label
-        fit_labels = {
-            0: "NOT FIT",
-            1: "POTENTIAL FIT",
-            2: "GOOD FIT"
-        }
-        
-        # Show the score prominently
-        st.markdown(f"## {fit_labels[fit_score]}")
+            # Display fit score with label
+            fit_labels = {
+                0: "NOT FIT",
+                1: "POTENTIAL FIT",
+                2: "GOOD FIT"
+            }
+            
+            # Show the score prominently
+            st.markdown(f"## {fit_labels[fit_score]}")
 
-        # Display assessment
-        st.markdown(assessment)
+            # Display assessment
+            st.markdown(assessment)
 
-        st.info(f"Analysis completed in {(summarization_time + assessment_time):.2f} seconds")
-        
-        # Add potential next steps based on the fit score
-        st.subheader("Recommended Next Steps")
-        
-        if fit_score == 2:
-            st.markdown("""
-            - Apply for this position as you appear to be a good match
-            - Prepare for interviews by focusing on your relevant experience
-            - Highlight your matching skills in your cover letter
-            """)
-        elif fit_score == 1:
-            st.markdown("""
-            - Consider applying but address skill gaps in your cover letter
-            - Emphasize transferable skills and relevant experience
-            - Prepare to discuss how you can quickly develop missing skills
-            """)
-        else:
-            st.markdown("""
-            - Look for positions better aligned with your current skills
-            - If interested in this field, focus on developing the required skills
-            - Consider similar roles with fewer experience requirements
-            """)
+            st.info(f"Analysis completed in {(summarization_time + assessment_time):.2f} seconds")
+            
+            # Add potential next steps based on the fit score
+            st.subheader("Recommended Next Steps")
+            
+            if fit_score == 2:
+                st.markdown("""
+                - Apply for this position as you appear to be a good match
+                - Prepare for interviews by focusing on your relevant experience
+                - Highlight your matching skills in your cover letter
+                """)
+            elif fit_score == 1:
+                st.markdown("""
+                - Consider applying but address skill gaps in your cover letter
+                - Emphasize transferable skills and relevant experience
+                - Prepare to discuss how you can quickly develop missing skills
+                """)
+            else:
+                st.markdown("""
+                - Look for positions better aligned with your current skills
+                - If interested in this field, focus on developing the required skills
+                - Consider similar roles with fewer experience requirements
+                """)
 
 if __name__ == "__main__":
     main()