Update app.py

app.py

@@ -48,6 +48,24 @@ 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):
     """
@@ -184,10 +202,29 @@ def explain_recommendation(student_info, top_recommendation):
     except Exception as e:
         return f"Error generating explanation: {str(e)}"
 
-def predict_career(desired_career, aggregate, interests, strengths, english, core_maths, science, social_studies, 
+def predict_career(desired_career, 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",
@@ -229,7 +266,10 @@ def predict_career(desired_career, aggregate, interests, strengths, english, cor
         # Get explanation
         explanation = explain_recommendation(student_info, top_recommendation)
         
-        return recommendations + "\n" + explanation
+        # Add aggregate information to the output
+        aggregate_info = f"Calculated Aggregate Score: {aggregate}\n\n"
+        
+        return aggregate_info + recommendations + "\n" + explanation
     except Exception as e:
         import traceback
         error_details = traceback.format_exc()
@@ -248,12 +288,11 @@ with gr.Blocks(title="Career Course Recommendation System") as demo:
     with gr.Row():
         with gr.Column(scale=2):
             gr.Markdown("### Student Information")
-            desired_career = gr.Dropdown(
-                choices=["Medicine", "Pharmacy", "Law", "Computer Science", "Engineering", "Business", "Nursing", "Agriculture", "Journalism", "Education"],
+            desired_career = gr.Textbox(
                 label="Desired Career",
-                info="Select your desired career path"
+                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")
             
@@ -290,7 +329,7 @@ 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 subjects.*
+            *Lower points are better. Aggregate is the sum of your best 4 subjects.*
             """)
             
             submit_btn = gr.Button("Get Recommendations", variant="primary", size="lg")
@@ -298,7 +337,7 @@ with gr.Blocks(title="Career Course Recommendation System") as demo:
     
     submit_btn.click(
         fn=predict_career,
-        inputs=[desired_career, aggregate, interests, strengths, english, core_maths, science, social_studies, 
+        inputs=[desired_career, interests, strengths, english, core_maths, science, social_studies, 
                 elective_maths, physics, biology, chemistry],
         outputs=output
     )