Update app.py

app.py

@@ -13,10 +13,11 @@ loaded_model = pickle.load(open("classroom_xgb.pkl", 'rb'))
 explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
 
 # Create the main function for server
-def main_func(Target, Admission_grade, Curricular_units_2nd_sem_grade, Previous_qualifications, Curricular_units_1st_sem_grade, Course, Curricular_units_2nd_sem_approved, Age_at_enrollment):
-    new_row = pd.DataFrame.from_dict({'Target':Target, 'Admission grade':Admission_grade,'Curricular units 2nd sem (grade)':Curricular_units_2nd_sem_grade,
-              'Previous qualifications':Previous_qualifications,'Curricular units 1st sem (grade)':Curricular_units_1st_sem_grade, 'Course':Course,'Curricular units 2nd sem (approved)':Curricular_units_2nd_sem_approved,
-              'Age at enrollment':Age_at_enrollment}).transpose() 
+def main_func(Target, Admission grade, Curricular units 2nd sem (grade), Previous qualification (grade), Curricular units 1st sem (grade), Course, Curricular units 2nd sem (approved), Age at enrollment):
+    new_row = pd.DataFrame.from_dict({'Target':Target,'Admission grade':AdmissionGrade,
+              'Curricular units 2nd sem (grade)':CurricularUnits2ndSemGrade,'Previous qualification (grade)':PreviousQualificationGrade,'Curricular units 1st sem (grade)':CurricularUnits1stSemGrade,
+              'Course':Course,'Curricular units 2nd sem (approved)':CurricularUnits2ndSemApproved,'Age at enrollment':AgeAtEnrollment,
+                                      orient = 'index').transpose() 
     
     prob = loaded_model.predict_proba(new_row)
     
@@ -29,11 +30,11 @@ def main_func(Target, Admission_grade, Curricular_units_2nd_sem_grade, Previous_
     local_plot = plt.gcf()
     plt.close()
     
-    return {"Low Chance": float(prob[0][0]), "High Chance": 1-float(prob[0][0])}, local_plot
+    return {"Dropout": float(prob[0][0]), "Graduate": 1-float(prob[0][0])}, local_plot
 
 # Create the UI
-title = "**Heart Attack Predictor & Interpreter** 🪐"
-description1 = """This app takes info from subjects and predicts their heart attack likelihood. Do not use for medical diagnosis."""
+title = "**Student Graduation Predictor & Interpreter** 🪐"
+description1 = """This app takes info from subjects and predicts their graduation likelihood."""
 
 description2 = """
 To use the app, click on one of the examples, or adjust the values of the factors, and click on Analyze. 🤞
@@ -45,28 +46,28 @@ with gr.Blocks(title=title) as demo:
     gr.Markdown("""---""")
     gr.Markdown(description2)
     gr.Markdown("""---""")
-    
-    Curricular_units_2nd_sem_grade = gr.Number(label="Curricular units 2nd sem (grade)", value=40)
-    Admission_grade = gr.Slider(label="Admission grade", minimum=0, maximum=1, value=1, step=1)
-    Previous_qualifications = gr.Slider(label="Previous qualifications", minimum=1, maximum=5, value=4, step=1)
-    Curricular_units_1st_sem_grade = gr.Slider(label="Curricular units 1st sem (grade)", minimum=1, maximum=5, value=4, step=1) 
-    Course = gr.Slider(label="Course", minimum=1, maximum=5, value=4, step=1) 
-    Curricular_units_2nd_sem_approved = gr.Slider(label="Curricular units 2nd sem (approved)", minimum=1, maximum=5, value=4, step=1)
-    Age_at_enrollment = gr.Slider(label="Age_at_enrollment", minimum=1, maximum=5, value=4, step=1)
-    
-    submit_btn = gr.Button("Analyze")
+    with gr.Row():
+        with gr.Column():
+        Target = gr.Number(label="Target Score", value=40)
+        AdmissionGrade = gr.Slider(label="AdmissionGrade Score", minimum=0, maximum=1, value=1, step=1)
+        PreviousQualificationGrade = gr.Slider(label="PreviousQualificationGrade Score", minimum=1, maximum=5, value=4, step=1)
+        CurricularUnits1stSemGrade = gr.Slider(label="CurricularUnits1stSemGrade Score", minimum=1, maximum=5, value=4, step=1) 
+        Course = gr.Slider(label="Course Score", minimum=1, maximum=5, value=4, step=1)
+        CurricularUnits2ndSemApproved = gr.Slider(label="CurricularUnits2ndSemApproved Score", minimum=1, maximum=5, value=4, step=1)
+        AgeAtEnrollment = gr.Slider(label="AgeAtEnrollment Score", minimum=1, maximum=5, value=4, step=1)
+        submit_btn = gr.Button("Analyze")
 
-    with gr.Column(visible=True) as output_col:
-        label = gr.Label(label = "Predicted Label")
-        local_plot = gr.Plot(label = 'Shap:')
+        with gr.Column(visible=True) as output_col:
+            label = gr.Label(label = "Predicted Label")
+            local_plot = gr.Plot(label = 'Shap:')
 
     submit_btn.click(
         main_func,
-        [Admission_grade, Curricular_units_2nd_sem_grade, Previous_qualifications, Curricular_units_1st_sem_grade, Course, Curricular_units_2nd_sem_approved, Age_at_enrollment],
-        [label,local_plot], api_name="Dropout_Predictor"
+        [Target, AdmissionGrade, CurricularUnits2ndSemGrade, PreviousQualificationGrade, CurricularUnits1stSemGrade, Course, CurricularUnits2ndSemApproved, AgeAtEnrollment],
+        [label,local_plot], api_name="Graduation_Predictor"
     )
     
     gr.Markdown("### Click on any of the examples below to see how it works:")
-    gr.Examples([[24,0,4,4,5,5,4,4,5,5,1,2,3], [24,0,4,4,5,3,3,2,1,1,1,2,3]], [Admission_grade, Curricular_units_2nd_sem_grade, Previous_qualifications, Curricular_units_1st_sem_grade, Course, Curricular_units_2nd_sem_approved, Age_at_enrollment], [label,local_plot], main_func, cache_examples=True)
+    gr.Examples([['Graduate',],119.6,13.000000,122.0,9773,5,18], [Target, AdmissionGrade, CurricularUnits2ndSemGrade, PreviousQualificationGrade, CurricularUnits1stSemGrade, Course, CurricularUnits2ndSemApproved, AgeAtEnrollment], [label,local_plot], main_func, cache_examples=True)
 
 demo.launch()