Update app.py

app.py

@@ -2,18 +2,13 @@ import joblib
 import pandas as pd
 import numpy as np
 import gradio as gr
-import sys
-
-# Workaround for numpy._core issue
-if not hasattr(np, '_core'):
-    sys.modules['numpy._core'] = np.core
 
 # Load your trained model
 model = joblib.load('trained_model.joblib')
 
 # Define grade to numeric conversion (same as before)
 def grade_to_numeric(grade):
-    if pd.isna(grade) or grade == "":
+    if pd.isna(grade) or grade == "" or grade is None:
         return np.nan
     grade_map = {
         "A1": 1, "B2": 2, "B3": 3, "C4": 4, "C5": 5, "C6": 6,
@@ -30,24 +25,24 @@ def preprocess_input(desired_career, aggregate, english, core_maths, science,
     
     # Create a dictionary with all inputs
     input_data = {
-        "Desired_Career": desired_career,
-        "Aggregate": aggregate,
-        "English": english,
-        "Core Maths": core_maths,
-        "Science": science,
-        "Social Studies": social_studies,
-        "Electives": electives,
-        "Elective Maths": elective_maths,
-        "Business Management": business_management,
-        "Government": government,
-        "Chemistry": chemistry,
-        "Physics": physics,
-        "Economics": economics,
-        "Visual Arts": visual_arts,
-        "Geography": geography,
-        "E-ICT": e_ict,
-        "Literature": literature,
-        "Biology": biology
+        "Desired_Career": str(desired_career) if desired_career is not None else "",
+        "Aggregate": int(aggregate) if aggregate is not None else 0,
+        "English": str(english) if english is not None else "",
+        "Core Maths": str(core_maths) if core_maths is not None else "",
+        "Science": str(science) if science is not None else "",
+        "Social Studies": str(social_studies) if social_studies is not None else "",
+        "Electives": str(electives) if electives is not None else "",
+        "Elective Maths": str(elective_maths) if elective_maths is not None else "",
+        "Business Management": str(business_management) if business_management is not None else "",
+        "Government": str(government) if government is not None else "",
+        "Chemistry": str(chemistry) if chemistry is not None else "",
+        "Physics": str(physics) if physics is not None else "",
+        "Economics": str(economics) if economics is not None else "",
+        "Visual Arts": str(visual_arts) if visual_arts is not None else "",
+        "Geography": str(geography) if geography is not None else "",
+        "E-ICT": str(e_ict) if e_ict is not None else "",
+        "Literature": str(literature) if literature is not None else "",
+        "Biology": str(biology) if biology is not None else ""
     }
     
     # Convert to DataFrame
@@ -71,46 +66,58 @@ def predict_career(desired_career, aggregate, english, core_maths, science,
                   physics=None, economics=None, visual_arts=None, geography=None,
                   e_ict=None, literature=None, biology=None):
     
-    # Preprocess input
-    processed_input = preprocess_input(
-        desired_career, aggregate, english, core_maths, science,
-        social_studies, electives, elective_maths, business_management,
-        government, chemistry, physics, economics, visual_arts, geography,
-        e_ict, literature, biology
-    )
-    
-    # Make prediction
-    prediction = model.predict(processed_input)
-    probabilities = model.predict_proba(processed_input)[0]
-    
-    # Get top 3 recommendations
-    class_indices = np.argsort(probabilities)[::-1][:3]
-    classes = model.classes_
-    recommendations = [
-        (classes[idx], float(probabilities[idx])) 
-        for idx in class_indices
-    ]
-    
-    # Format output
-    output = "\n".join(
-        [f"{course}: {prob*100:.1f}%" for course, prob in recommendations]
-    )
-    
-    return output
+    try:
+        # Preprocess input
+        processed_input = preprocess_input(
+            desired_career, aggregate, english, core_maths, science,
+            social_studies, electives, elective_maths, business_management,
+            government, chemistry, physics, economics, visual_arts, geography,
+            e_ict, literature, biology
+        )
+        
+        # Make prediction
+        prediction = model.predict(processed_input)
+        probabilities = model.predict_proba(processed_input)[0]
+        
+        # Get top 3 recommendations
+        class_indices = np.argsort(probabilities)[::-1][:3]
+        classes = model.classes_
+        recommendations = [
+            (str(classes[idx]), float(probabilities[idx])) 
+            for idx in class_indices
+        ]
+        
+        # Format output
+        output = "\n".join(
+            [f"{course}: {prob*100:.1f}%" for course, prob in recommendations]
+        )
+        
+        return output
+    except Exception as e:
+        return f"Error processing input: {str(e)}"
 
 # Create Gradio interface
 inputs = [
     gr.Dropdown(["Medicine", "Pharmacy", "Law", "Computer Science", "Engineering", 
                 "Business", "Nursing", "Agriculture", "Journalism", "Education"], 
                label="Desired Career"),
-    gr.Number(label="Aggregate Score"),
+    gr.Number(label="Aggregate Score", precision=0),
     gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9"], label="English"),
     gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9"], label="Core Maths"),
     gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9"], label="Science"),
     gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9"], label="Social Studies"),
     gr.Textbox(label="Electives (comma separated)"),
     gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Elective Maths (optional)"),
-    # Add other optional subjects similarly
+    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Business Management (optional)"),
+    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Government (optional)"),
+    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Chemistry (optional)"),
+    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Physics (optional)"),
+    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Economics (optional)"),
+    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Visual Arts (optional)"),
+    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Geography (optional)"),
+    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="E-ICT (optional)"),
+    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Literature (optional)"),
+    gr.Dropdown(["A1", "B2", "B3", "C4", "C5", "C6", "D7", "E8", "F9", None], label="Biology (optional)"),
 ]
 
 outputs = gr.Textbox(label="Recommended Courses")
@@ -123,4 +130,5 @@ interface = gr.Interface(
     description="Enter your academic information to get career recommendations"
 )
 
-interface.launch()
+if __name__ == "__main__":
+    interface.launch()