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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, OneHotEncoder, LabelEncoder
# Load the trained model and scaler objects from file
REPO_ID = "Hemg/marketpredict" # hugging face repo ID
MoDEL_FILENAME = "stx.joblib" # model file name
SCALER_FILENAME ="scaler.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(Year,Instagram_Advertising,Facebook_Advertising,Event_Expenses,Internet_Expenses):
# Prepare input data represents independent variables for house prediction
input_data = [[Year,Instagram_Advertising,Facebook_Advertising,Event_Expenses,Internet_Expenses]]
# Get the feature names from the Gradio interface inputs
feature_names = ["Year","Instagram_Advertising","Facebook_Advertising","Event_Expenses","Internet_Expenses"]
# Create a Pandas DataFrame with the input data and feature names
input_df = pd.DataFrame(input_data, columns=feature_names)
input_df = pd.DataFrame(input_data, columns=feature_names)
df = encode_categorical_columns(input_df)
# Scale the input data using the loaded scaler
scaled_input = scaler.transform(df)
# Make predictions using the loaded model
prediction = model.predict(scaled_input)[0]
return f"No of Forecast Admitted students: {prediction:,.2f}"
# Create the Gradio app
iface = gr.Interface(
fn=predict_performance,
inputs=[
gr.Slider(minimum=2024, maximum=2025, step=1, label="Year"),
gr.Slider(minimum=10000, maximum=50000, step=500, label="Instagram_Advertising"),
gr.Slider(minimum=10000, maximum=50000, step=500, label="Facebook_Advertising"),
gr.Slider(minimum=20000, maximum=50000,step=500,label="Event_Expenses"),
gr.Slider(minimum=5000, maximum=25000,step=500,label="Internet_EXpenses")
],
outputs="text",
title="Student_Admitted_Forecast",
description="Student_Admitted"
)
# Run the app
if __name__ == "__main__":
iface.launch(share=True) |