Update app.py

app.py

@@ -48,24 +48,6 @@ def grade_to_numeric(grade):
     }
     return grade_map.get(grade, np.nan)
 
-# Function to calculate aggregate score based on grades
-def calculate_aggregate(grades_dict):
-    # Filter out empty grades
-    valid_grades = {k: grade_to_numeric(v) for k, v in grades_dict.items() if v and v != ""}
-    
-    # Need at least 4 grades to calculate aggregate
-    if len(valid_grades) < 4:
-        return None
-    
-    # Sort grades by value (lower is better)
-    sorted_grades = sorted(valid_grades.values())
-    
-    # Take the best (lowest) 4 grades for aggregate
-    best_grades = sorted_grades[:4]
-    
-    # Calculate aggregate
-    return sum(best_grades)
-
 # Function to extract interests and strengths into separate columns
 def extract_traits(df, column_name, prefix, all_traits=None):
     """
@@ -177,18 +159,7 @@ def explain_recommendation(student_info, top_recommendation):
         # Career alignment
         explanation += f"- Your career interest in {student_info['Desired_Career']}\n"
 
-        # Academic strengths
-        subjects = []
-        if grade_to_numeric(student_info.get('Core Maths', '')) <= 3:
-            subjects.append("Mathematics")
-        if grade_to_numeric(student_info.get('English', '')) <= 3:
-            subjects.append("English")
-        if grade_to_numeric(student_info.get('Science', '')) <= 3:
-            subjects.append("Science")
-        if subjects:
-            explanation += f"- Your strong performance in {', '.join(subjects)}\n"
-
-        # Interests and strengths match
+        # Interests match
         explanation += f"- Your interests in {student_info['Interests']}\n"
         explanation += f"- Your strengths in {student_info['Strengths']}\n"
 
@@ -202,29 +173,10 @@ def explain_recommendation(student_info, top_recommendation):
     except Exception as e:
         return f"Error generating explanation: {str(e)}"
 
-def predict_career(desired_career, interests, strengths, english, core_maths, science, social_studies, 
+def predict_career(desired_career, aggregate, interests, strengths, english, core_maths, science, social_studies, 
                   elective_maths, physics, biology, chemistry):
     
     try:
-        # Collect all grades in a dictionary
-        grades = {
-            "English": english,
-            "Core Maths": core_maths,
-            "Science": science,
-            "Social Studies": social_studies,
-            "Elective Maths": elective_maths,
-            "Physics": physics,
-            "Biology": biology,
-            "Chemistry": chemistry
-        }
-        
-        # Calculate aggregate automatically
-        aggregate = calculate_aggregate(grades)
-        
-        # If not enough grades were provided
-        if aggregate is None:
-            return "Error: Please provide at least 4 subject grades to calculate aggregate score."
-        
         # Create student data dictionary with all required fields
         student_info = {
             "StudentID": "STU_TEMP",
@@ -266,10 +218,7 @@ def predict_career(desired_career, interests, strengths, english, core_maths, sc
         # Get explanation
         explanation = explain_recommendation(student_info, top_recommendation)
         
-        # Add aggregate information to the output
-        aggregate_info = f"Calculated Aggregate Score: {aggregate}\n\n"
-        
-        return aggregate_info + recommendations + "\n" + explanation
+        return recommendations + "\n" + explanation
     except Exception as e:
         import traceback
         error_details = traceback.format_exc()
@@ -293,8 +242,14 @@ with gr.Blocks(title="Career Course Recommendation System") as demo:
                 placeholder="Enter your desired career path (e.g. Medicine, Computer Science, Engineering)",
                 info="Enter your desired career path"
             )
+            aggregate = gr.Slider(minimum=6, maximum=37, value=15, step=1, label="Aggregate Score", info="Lower is better (6 is best, 37 is worst)")
             interests = gr.Textbox(label="Interests (comma separated)", placeholder="Reading,Dancing,Physics", info="List your interests separated by commas")
-            strengths = gr.Textbox(label="Strengths (comma separated)", placeholder="Communication,Creativity", info="List your strengths separated by commas")
+            strengths = gr.Textbox(
+                label="Strengths (comma separated)", 
+                placeholder="Communication,Creativity", 
+                info="List your strengths separated by commas",
+                value="Communication,Creativity,Logical Reasoning,Analytical Thinking"
+            )
             
             gr.Markdown("### Core Subjects (Required)")
             with gr.Row():
@@ -329,7 +284,7 @@ with gr.Blocks(title="Career Course Recommendation System") as demo:
             - E8: Pass (8 points)
             - F9: Fail (9 points)
             
-            *Lower points are better. Aggregate is the sum of your best 4 subjects.*
+            *Lower points are better. Aggregate is the sum of your best subjects.*
             """)
             
             submit_btn = gr.Button("Get Recommendations", variant="primary", size="lg")
@@ -337,7 +292,7 @@ with gr.Blocks(title="Career Course Recommendation System") as demo:
     
     submit_btn.click(
         fn=predict_career,
-        inputs=[desired_career, interests, strengths, english, core_maths, science, social_studies, 
+        inputs=[desired_career, aggregate, interests, strengths, english, core_maths, science, social_studies, 
                 elective_maths, physics, biology, chemistry],
         outputs=output
     )