Create app.py

app.py

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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 = encode_categorical_columns(input_df)
+
+    # Scale the input data using the loaded scaler
+    scaled_input = scaler.transform(input_df)
+
+    # Make predictions using the loaded model
+    prediction = model.predict(scaled_input)[0]
+
+    # Return the result as HTML with custom styling (green color and larger font)
+    return f'<p style="font-size: 24px; color: green;">Forecast no of. Students admission: {prediction:,.0f}</p>'
+
+# Create the Gradio app
+iface = gr.Interface(
+    fn=predict_performance,
+    inputs=[
+        gr.Slider(minimum=2024, maximum=2025, step=1, label="Year",info="The forecasted Year"),
+        gr.Slider(minimum=10000, maximum=45000, step=500, label="Instagram_Advertising", info="How much do you spend on Instagram ads Yearly($)?"),
+        gr.Slider(minimum=10000, maximum=75000, step=500, label="Facebook_Advertising", info="How much do you spend on Facebook ads Yearly($)?"),
+        gr.Slider(minimum=20000, maximum=100000, step=500, label="Event_Expenses", info="What’s your typical budget for events($)?"),
+        gr.Slider(minimum=5000, maximum=45000, step=500, label="Internet_Expenses", info="How much do you spend on internet Yearly($)?")
+    ],
+    outputs=gr.HTML(),  # Specify the output as HTML
+    title="Student Admission Forecast",
+    description="Forecast of chances of student admission based on marketing expenditures"
+)
+
+# Run the app
+if __name__ == "__main__":
+    iface.launch(share=True)