File size: 6,422 Bytes
1013508 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
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
# Create styled HTML output
# html_template = """
# <div style='text-align: center; padding: 20px;'>
# <div style='font-family: Arial, sans-serif; font-size: 24px; margin-bottom: 15px;'>
# Admission Probability: <span style='font-weight: bold;'>{:.1f}%</span>
# </div>
# <div style='{style}'>
# {message}
# </div>
# </div>
# """
# Create styled HTML output
html_template = """
<div style='text-align: center; padding: 20px;'>
<div style='{style}'>
{message}
</div>
</div>
"""
if prediction_percentage > 50:
style = "font-family: Arial, sans-serif; font-size: 32px; color: #28a745; font-weight: bold;"
message = "High chance of admission"
elif prediction_percentage < 50:
style = "font-family: Arial, sans-serif; font-size: 32px; color: #dc3545; font-weight: bold; text-transform: uppercase;"
message = "Lower chance of admission"
else: # exactly 50
style = "font-family: Arial, sans-serif; font-size: 32px; color: #ffc107; font-weight: bold;"
message = "Moderate chance of admission"
return html_template.format(prediction_percentage, style=style, message=message)
except Exception as e:
return f"<div style='color: red; font-family: Arial, sans-serif;'>Error in prediction: {str(e)}</div>"
# Update the 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="What is the name of the last college you attended?"),
gr.Slider(minimum=2, maximum=3, label="GPA", info="What is your GPA (Grade Point Average) of +2 ?"),
gr.Slider(minimum=2024, maximum=2026, step=1, label="Year", info="What is your intended year of admission?"),
#gr.Radio([2024, 2025, 2026], 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=gr.HTML(), # Changed to HTML output
title="Student Admission Prediction",
description="Predict the probability of student admission",
css="body { font-family: Arial, sans-serif; }"
)
if __name__ == "__main__":
iface.launch(share=True) |