Update app.py

app.py

@@ -6,16 +6,13 @@ from huggingface_hub import hf_hub_download
 from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder
 
 # Load the trained model and scaler objects from file
+REPO_ID = "Hemg/modelxxx"  # hugging face repo ID
+MODEL_FILENAME = "predjob.joblib"  # model file name
+SCALER_FILENAME = "scalejob.joblib"  # scaler file name
 
-
-REPO_ID = "Hemg/modelxxx" # hugging face  repo ID
-MoDEL_FILENAME = "predjob.joblib" # modlinearel file name
-SCALER_FILENAME ="scalejob.joblib" # scaler file name
-
-model = joblib.load(hf_hub_download(repo_id=REPO_ID, filename=MoDEL_FILENAME))
-
+model = joblib.load(hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME))
 scaler = joblib.load(hf_hub_download(repo_id=REPO_ID, filename=SCALER_FILENAME))
- 
+
 def encode_categorical_columns(df):
     label_encoder = LabelEncoder()
     ordinal_columns = df.select_dtypes(include=['object']).columns
@@ -29,28 +26,27 @@ def encode_categorical_columns(df):
     return df
 
 # Define the prediction function
-def predict_performance(Location,Course,College,Faculty,Source,Event,Presenter,Visited Parent,Visited College for Inquiry,Attended Any Event,College Fee,GPA,Year):
-    
+def predict_performance(Location, Course, College, Faculty, Source, Event, Presenter, Visited_Parent, Visited_College_for_Inquiry, Attended_Any_Event, College_Fee, GPA, Year):
     # Debug print 1: Print all input values
     print("\nInput Values:")
     print(f"Location: {Location}")
     print(f"College Fee: {College_Fee}")
     print(f"GPA: {GPA}")
     print(f"Year: {Year}")
-    print(f"Course: {Course_Interested}")
+    print(f"Course: {Course}")
     print(f"Faculty: {Faculty}")
     print(f"Source: {Source}")
-    print(f"Visited College for Inquiry: {visited_college_for_inquiry_only}")
-    print(f"Event : {attended_event_name}")
-    print(f"Attended Any vents: {ttended any_events}")
+    print(f"Visited College for Inquiry: {Visited_College_for_Inquiry}")
+    print(f"Event: {Event}")
+    print(f"Attended Any Event: {Attended_Any_Event}")
     print(f"Presenter: {Presenter}")
-    print(f"Visited with parents: {visited_with_parents}")
+    print(f"Visited with Parents: {Visited_Parent}")
 
-    input_data = [Location,Course,College,Faculty,Source,Event,Presenter,Visited Parent,Visited College for Inquiry,Attended Any Event,College Fee,GPA,Year
+    input_data = [Location, Course, College, Faculty, Source, Event, Presenter, Visited_Parent, Visited_College_for_Inquiry, Attended_Any_Event, College_Fee, GPA, Year]
 
-    feature_names = ["Location", "Course", "College", "Faculty", "Source", "Event", "Presenter", "Visited Parent", "Visited College for Inquiry", "Attended Any Event", "College Fee", "GPA", "Year"]
+    feature_names = ["Location", "Course", "College", "Faculty", "Source", "Event", "Presenter", "Visited_Parent", "Visited_College_for_Inquiry", "Attended_Any_Event", "College_Fee", "GPA", "Year"]
     
-    input_df = pd.DataFrame(input_data, columns=feature_names)
+    input_df = pd.DataFrame([input_data], columns=feature_names)
     
     # Debug print 2: Show DataFrame before encoding
     print("\nDataFrame before encoding:")
@@ -64,23 +60,21 @@ def predict_performance(Location,Course,College,Faculty,Source,Event,Presenter,V
     
     scaled_input = scaler.transform(df)
     
-    # # Debug print 4: Show scaled input
-    # print("\nScaled input:")
-    # print(scaled_input)
+    # Make the prediction
+    prediction = model.predict(scaled_input)[0]
     
-    # prediction = model.predict(scaled_input)[0]
+    # Debug print 4: Show prediction details
+    print("\nPrediction details:")
+    print(f"Raw prediction: {prediction}")
+    prediction_probability = 1 / (1 + np.exp(-prediction))
+    print(f"Probability: {prediction_probability}")
     
-    # # Debug print 5: Show prediction details
-    # print("\nPrediction details:")
-    # print(f"Raw prediction: {prediction}")
-    # prediction_probability = 1 / (1 + np.exp(-prediction))
-    # print(f"Probability: {prediction_probability}")
     prediction_percentage = prediction_probability * 100
     print(f"Percentage: {prediction_percentage}")
     
     return f"Chance of Admission: {prediction_percentage:.1f}%"
 
- 
+# Create Gradio interface
 iface = gr.Interface(
     fn=predict_performance,
     inputs=[
@@ -90,19 +84,19 @@ iface = gr.Interface(
         gr.Slider(minimum=2019, maximum=2025, step=1, label="Year"),
         gr.Radio(["MSc IT & Applied Security", "BSc (Hons) Computing", "BSc (Hons) Computing with Artificial Intelligence", 
                  "BSc (Hons) Computer Networking & IT Security", "BSc (Hons) Multimedia Technologies", "MBA", 
-                 "BA (Hons) Accounting &  Finance", "BA (Hons) Business Administration"], label="Course_Interested"),
+                 "BA (Hons) Accounting & Finance", "BA (Hons) Business Administration"], label="Course"),
         gr.Radio(["Science", "Management", "Humanities"], label="Faculty"),
         gr.Radio(["Event", "Facebook", "Instagram", "Offline", "Recommendation"], label="Source"),
-        gr.Radio(["Yes", "No"], label="visited_college_for_inquery_only"),
+        gr.Radio(["Yes", "No"], label="Visited College for Inquiry"),
         gr.Radio(["New Year", "Dashain", "Orientation", "Fresher's Party", "Holi Festival", "Welcome Ceremony"], 
-                label="attended_event_name"),  # Changed from Slider to Radio
-        gr.Radio(["Yes", "No"], label="attended_any_event"),
+                 label="Event"),
+        gr.Radio(["Yes", "No"], label="Attended Any Event"),
         gr.Radio(["Ram", "Gita", "Manish", "Shyam", "Raj", "Hari", "Rina", "Shree"], label="Presenter"),
-        gr.Radio(["Yes", "No"], label="visited_with_parents")
+        gr.Radio(["Yes", "No"], label="Visited with Parents")
     ],
     outputs="text",
-    title="chances of student admission",
-    description="chances of student admission"
+    title="Chance of Student Admission",
+    description="Predict the chances of a student's admission based on various inputs."
 )
 
 # Run the app