appx.py

import gradio as gr
import joblib
import numpy as np
import pandas as pd
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 = "studentpredict.joblib" # model file name
SCALER_FILENAME ="studentscaler.joblib" # scaler file name

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

    for col in ordinal_columns:
        df[col] = label_encoder.fit_transform(df[col])

    nominal_columns = df.select_dtypes(include=['object']).columns.difference(ordinal_columns)
    df = pd.get_dummies(df, columns=nominal_columns, drop_first=True)

    return df

# Define the prediction function
def predict_performance(Location, College_Fee,College, GPA, Year, Course_Interested, Faculty, Source, 
                       Visited_College_for_Inquiry_Only, Event, Attended_Any_Events, 
                       Presenter, Visited_Parents):
    
    input_data = [[Location, College_Fee,College, GPA, Year, Course_Interested, Faculty, Source,
                 Visited_College_for_Inquiry_Only, Event, Attended_Any_Events,
                 Presenter, Visited_Parents]]

    feature_names = ["Location", "College Fee", "GPA", "Year", "Course Interested",
                 "Faculty", "Source", "Visited College for Inquiry Only", "Event",
                 "Attended Any Events", "College", "Presenter", "Visited Parents"]
    
    input_df = pd.DataFrame(input_data, columns=feature_names)
    
    # Debug print 2: Show DataFrame before encoding
    print("\nDataFrame before encoding:")
    print(input_df)
    
    df = encode_categorical_columns(input_df)
    
    # Debug print 3: Show DataFrame after encoding
    print("\nDataFrame after encoding:")
    print(df)
    
    # Ensure the DataFrame columns match the scaler's expected input
    df = df.reindex(columns=scaler.feature_names_in_, fill_value=0)
    
    scaled_input = scaler.transform(df)
    
    # Debug print 4: Show scaled input
    print("\nScaled input:")
    print(scaled_input)
    
    prediction = model.predict(scaled_input)[0]
    
    # 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}%"
 
iface = gr.Interface(
    fn=predict_performance,
    inputs=[
        gr.Radio(["Kathmandu", "Bhaktapur", "Lalitpur", "Kritipur"], label="Location"),
        gr.Slider(minimum=1000000, maximum=1700000, label="College Fee"),
        gr.Slider(minimum=2, maximum=3, label="GPA"),
        gr.Slider(minimum=2024, maximum=2024, 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"),
        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(["New Year", "Dashain", "Orientation", "Fresher's Party", "Holi Festival", "Welcome Ceremony"], 
                label="attended_event_name"),
        gr.Radio(["Yes", "No"], label="attended_any_event"),
        gr.Radio(["Trinity", "CCRC", "KMC", "SOS", "ISMT", "St. Xavier's", "Everest", "Prime"], label="College"),
        gr.Radio(["Ram", "Gita", "Manish", "Shyam", "Raj", "Hari", "Rina", "Shree"], label="Presenter"),
        gr.Radio(["Yes", "No"], label="visited_with_parents")  # Removed the incorrect list here
    ],
    outputs="text",
    title="chances of student admission",
    description="chances of student admission"
)

# Run the app
if __name__ == "__main__":
    iface.launch(share=True)