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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, LabelEncoder
REPO_ID = "Hemg/modelxxx"
MoDEL_FILENAME = "studentpredict.joblib"
SCALER_FILENAME = "studentscaler.joblib"
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
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):
try:
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", "College", "GPA", "Year", "Course Interested",
"Faculty", "Source", "Visited College for Inquiry Only", "Event",
"Attended Any Events", "Presenter", "Visited Parents"]
input_df = pd.DataFrame(input_data, columns=feature_names)
df = encode_categorical_columns(input_df)
df = df.reindex(columns=scaler.feature_names_in_, fill_value=0)
scaled_input = scaler.transform(df)
# Get probability prediction
probabilities = model.predict_proba(scaled_input)[0]
# Take the probability of positive class (usually index 1)
admission_probability = probabilities[1]
# Ensure the probability is between 0 and 1
admission_probability = np.clip(admission_probability, 0, 1)
# Convert to percentage
prediction_percentage = admission_probability * 100
# Format the output
confidence_level = ""
if prediction_percentage > 50:
confidence_level = "High chance of admission"
elif prediction_percentage == 50:
confidence_level = "Moderate chance of admission"
else:
confidence_level = "Lower chance of admission"
return confidence_level
#return f"Admission Probability: {prediction_percentage:.1f}%\n{confidence_level}"
except Exception as e:
return f"Error in prediction: {str(e)}"
# Create Gradio interface
iface = gr.Interface(
fn=predict_performance,
inputs=[
gr.Radio(["Kathmandu", "Bhaktapur", "Lalitpur", "Kritipur"], label="Location",info="What is your current location?"),
gr.Slider(minimum=1000000, maximum=1700000,step=100000,label="College Fee", info="What 's the the total bachelor fee for the course you want to enroll?"),
gr.Radio(["Trinity", "CCRC", "KMC", "SOS", "ISMT", "St. Xavier's", "Everest", "Prime"], label="College", info=""),
gr.Slider(minimum=2, maximum=3, label="GPA", info="What is your GPA (Grade Point Average) of +2 ?"),
gr.Slider(minimum=2024, maximum=2024, step=1, label="Year", info="What is your intended year of admission?"),
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", info="Which course are you most interested in?"),
gr.Radio(["Science", "Management", "Humanities"], label="Faculty", info="what is your last stream ?"),
gr.Radio(["Event", "Facebook", "Instagram", "Offline", "Recommendation"], label="Source",info="How did you first hear about this college?"),
gr.Radio(["Yes", "No"], label="visited_college_for_inquery_only", info="Have you visited the college you're interested in for an inquiry or consultation?"),
gr.Radio(["Yes", "No"], label="attended_any_event", info="Have you attended any events organized by the college you're interested in?"),
gr.Radio(["New Year", "Dashain", "Orientation", "Fresher's Party", "Holi Festival", "Welcome Ceremony"],
label="attended_event_name", info="If yes, which events did you attend?" ),
gr.Radio(["Ram", "Gita", "Manish", "Shyam", "Raj", "Hari", "Rina", "Shree"], label="Presenter", info="who is the counser that help you while in counseling?"),
gr.Radio(["Yes", "No"], label="visited_with_parents", info="Did you visit the college with your parents?")
],
outputs="text",
title="Student Admission Prediction",
description="Predict the probability of student admission for Bachelor Study"
)
if __name__ == "__main__":
iface.launch(share=True)
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