Update app.py

app.py

@@ -1,58 +1,58 @@
-#gradio app 
-import gradio as gr
-import pandas as pd
-import pickle
-import numpy as np
-
-# Load the Model
-with open("student_rf_pipeline.pkl", "rb") as f:
-    model = pickle.load(f)
-
-# The Logic Function
-def predict_gpa(gender, age, address, famsize, 
-                Pstatus, M_Edu, F_Edu, M_Job, F_Job, 
-                relationship, smoker, tuition_fee, time_friends,
-                  ssc_result):
-    
-    input_df = pd.DataFrame([[
-        gender, age, address, famsize, Pstatus, 
-        M_Edu, F_Edu, M_Job, F_Job, relationship, 
-        smoker, tuition_fee, time_friends, ssc_result
-
-    ]],
-      columns=[
-        'gender', 'age', 'address', 'famsize', 'Pstatus', 'M_Edu', 'F_Edu', 'M_Job', 'F_Job', 'relationship', 'smoker', 'tuition_fee', 'time_friends', 'ssc_result'
-    ])
-    
-    # Predict
-    prediction = model.predict(input_df)[0]
-    
-    # Return formatted result (Clipped 0-5)
-    return f"Predicted HSC Result: {np.clip(prediction, 0, 5):.2f}"
-
-# 3. The App Interface
-
-inputs = [
-    gr.Radio(["M", "F"], label="Gender"),
-    gr.Number(label="Age", value=18),
-    gr.Radio(["Urban", "Rural"], label="Address"),
-    gr.Radio(["GT3", "LE3"], label="Family Size"),
-    gr.Radio(["Together", "Apart"], label="Parent Status"),
-    gr.Slider(0, 4, step=1, label="Mother's Edu"),
-    gr.Slider(0, 4, step=1, label="Father's Edu"),
-    gr.Dropdown(["At_home", "Health", "Other", "Services", "Teacher"], label="Mother's Job"),
-    gr.Dropdown(["Teacher", "Other", "Services", "Health", "Business", "Farmer"], label="Father's Job"),
-    gr.Radio(["Yes", "No"], label="Relationship"),
-    gr.Radio(["Yes", "No"], label="Smoker"),
-    gr.Number(label="Tuition Fee"),
-    gr.Slider(1, 5, step=1, label="Time with Friends"),
-    gr.Number(label="SSC Result (GPA)")
-]
-
-app = gr.Interface(
-    fn=predict_gpa,
-      inputs=inputs,
-        outputs="text", 
-        title="Student HSC Result Predictor")
-
+#gradio app 
+import gradio as gr
+import pandas as pd
+import pickle
+import numpy as np
+
+# Load the Model
+with open("student_rf_pipeline.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# The Logic Function
+def predict_gpa(gender, age, address, famsize, 
+                Pstatus, M_Edu, F_Edu, M_Job, F_Job, 
+                relationship, smoker, tuition_fee, time_friends,
+                  ssc_result):
+    
+    input_df = pd.DataFrame([[
+        gender, age, address, famsize, Pstatus, 
+        M_Edu, F_Edu, M_Job, F_Job, relationship, 
+        smoker, tuition_fee, time_friends, ssc_result
+
+    ]],
+      columns=[
+        'gender', 'age', 'address', 'famsize', 'Pstatus', 'M_Edu', 'F_Edu', 'M_Job', 'F_Job', 'relationship', 'smoker', 'tuition_fee', 'time_friends', 'ssc_result'
+    ])
+    
+    # Predict
+    prediction = model.predict(input_df)[0]
+    
+    # Return formatted result (Clipped 0-5)
+    return f"Predicted HSC Result: {np.clip(prediction, 0, 5):.2f}"
+
+# 3. The App Interface
+
+inputs = [
+    gr.Radio(["M", "F"], label="Gender"),
+    gr.Number(label="Age", value=18),
+    gr.Radio(["Urban", "Rural"], label="Address"),
+    gr.Radio(["GT3", "LE3"], label="Family Size"),
+    gr.Radio(["Together", "Apart"], label="Parent Status"),
+    gr.Slider(0, 4, step=1, label="Mother's Edu"),
+    gr.Slider(0, 4, step=1, label="Father's Edu"),
+    gr.Dropdown(["At_home", "Health", "Other", "Services", "Teacher"], label="Mother's Job"),
+    gr.Dropdown(["Teacher", "Other", "Services", "Health", "Business", "Farmer"], label="Father's Job"),
+    gr.Radio(["Yes", "No"], label="Relationship"),
+    gr.Radio(["Yes", "No"], label="Smoker"),
+    gr.Number(label="Tuition Fee"),
+    gr.Slider(1, 5, step=1, label="Time with Friends"),
+    gr.Number(label="SSC Result (GPA)")
+]
+
+app = gr.Interface(
+    fn=predict_gpa,
+      inputs=inputs,
+        outputs="text", 
+        title="Student's HSC Result Predictor")
+
 app.launch(share=True)