app.py

app.py file uploaded

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/studentApp" # 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(Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,SampleQuestions_Practiced):
+    # Prepare input data represents independent variables for house prediction
+    input_data = [[Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,SampleQuestions_Practiced]]
+
+    # Get the feature names from the Gradio interface inputs
+    feature_names = ['Hours_Studied','Previous_Scores','Extracurricular_Activities','Sleep_Hours','SampleQuestions_Practiced']
+    # 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"Performance_Index: {prediction:,.2f}" 
+
+# Create the Gradio app
+iface = gr.Interface(
+    fn=predict_performance,
+    inputs=[
+        gr.Slider(minimum=1, maximum=10, step=1, label="Hours_Studied",info="How many hours did you study last week?"),
+        gr.Slider(minimum=40, maximum=100, step=1, label="Previous_Scores",info="What were your previous academic scores in your most recent exams or assessments?"),
+        #gr.Slider(minimum=1, maximum=100, step=1, label="Extracurricular_Activities",info="Are you involved in any extracurricular activities outside of your regular coursework?"),
+        gr.Radio(["Yes", "No"], label="Extracurricular_Activities",info="Are you involved in any extracurricular activities outside of your regular coursework?"),
+        gr.Slider(minimum=3, maximum=10, step=1, label="Sleep_Hours",info="On average, how many hours of sleep do you get per night?"),
+        gr.Slider(minimum=1, maximum=10, step=1, label="SampleQuestions_Practiced",info="Have you been practicing any sample questions or past exam papers as part of your study routine?")
+    ],
+    outputs="text",
+    title="Student_performance_prediction",
+    description="Predict Performance_Index"
+)
+
+# Run the app
+if __name__ == "__main__":
+    iface.launch(share=True)