Update app.py

app.py

@@ -43,15 +43,7 @@ def load_models():
             truncation=True
         )
         
-        # Load T5-small model for evaluation with optimized settings
-        models['evaluator'] = pipeline(
-            "text2text-generation", 
-            model="google-t5/t5-small",
-            max_length=200,
-            num_beams=2,
-            early_stopping=True
-        )
-        
+        # We don't need T5 model anymore since we're using template-based feedback
         return models
 
 # Preload models immediately when app starts
@@ -397,7 +389,7 @@ def calculate_google_match_score(candidate_summary):
 #####################################
 def generate_template_feedback(category_scores):
     """
-    Generate comprehensive template-based feedback without using ML model for speed.
+    Generate comprehensive template-based feedback without using ML model for speed and reliability.
     """
     start_time = time.time()
     
@@ -504,53 +496,6 @@ def generate_template_feedback(category_scores):
     
     return feedback, execution_time
 
-#####################################
-# Function: Generate Aspect-Based Feedback with T5 - Enhanced with Fallback
-#####################################
-@st.cache_data(show_spinner=False)
-def generate_aspect_feedback(candidate_summary, category_scores, _evaluator=None):
-    """
-    Use T5-small model to generate feedback with robust fallback to template-based feedback.
-    """
-    start_time = time.time()
-    
-    evaluator = _evaluator or models['evaluator']
-    
-    # Sort categories by score
-    sorted_categories = sorted(category_scores.items(), key=lambda x: x[1], reverse=True)
-    top_categories = sorted_categories[:2]
-    bottom_categories = sorted_categories[-2:]
-    
-    # Create a more explicit prompt for T5
-    prompt = f"""
-Generate a complete paragraph evaluating a job candidate for Google.
-The candidate is strong in: {', '.join([cat for cat, _ in top_categories])}.
-The candidate needs improvement in: {', '.join([cat for cat, _ in bottom_categories])}.
-Start with 'This candidate' and write at least 3 sentences about their fit for Google.
-"""
-    
-    # Generate focused feedback with error handling
-    try:
-        feedback_result = evaluator(prompt, max_length=200, do_sample=False)
-        feedback = feedback_result[0]['generated_text']
-        
-        # Validate the response - ensure it's not empty or too short
-        if len(feedback.strip()) < 20 or feedback.strip() == "This candidate" or feedback.strip() == "This candidate.":
-            # Fall back to template-based if T5 output is too short
-            return generate_template_feedback(category_scores)
-            
-        # Ensure third-person tone
-        if not any(feedback.lower().startswith(start) for start in ["the candidate", "this candidate"]):
-            feedback = f"This candidate {feedback}"
-    except Exception as e:
-        # Fall back to template if there's an error
-        print(f"Error generating T5 feedback: {e}")
-        return generate_template_feedback(category_scores)
-    
-    execution_time = time.time() - start_time
-    
-    return feedback, execution_time
-
 #####################################
 # Main Streamlit Interface - with Progress Reporting
 #####################################
@@ -571,10 +516,6 @@ with st.expander("Google's Requirements", expanded=False):
 # File uploader
 uploaded_file = st.file_uploader("Upload your resume (.docx, .doc, or .txt)", type=["docx", "doc", "txt"])
 
-# Add a checkbox for template-based feedback (faster)
-use_template_feedback = st.checkbox("Use faster template-based feedback (no ML)", value=False, 
-                                   help="Generate feedback using pre-defined templates instead of T5 model")
-
 # Process button with optimized flow
 if uploaded_file is not None and st.button("Analyze My Google Fit"):
     # Create a placeholder for the progress bar
@@ -603,13 +544,8 @@ if uploaded_file is not None and st.button("Analyze My Google Fit"):
         status_text.text("Step 3/3: Calculating Google fit scores...")
         overall_score, category_scores, score_breakdown = calculate_google_match_score(summary)
         
-        # Choose feedback generation method based on checkbox
-        if use_template_feedback:
-            feedback, feedback_time = generate_template_feedback(category_scores)
-        else:
-            feedback, feedback_time = generate_aspect_feedback(
-                summary, category_scores, _evaluator=models['evaluator']
-            )
+        # Always use template-based feedback (more reliable)
+        feedback, feedback_time = generate_template_feedback(category_scores)
             
         progress_bar.progress(100)