Create app.py

app.py

@@ -0,0 +1,83 @@
+import gradio as gr
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+import os
+
+# Define the model path and file name on Hugging Face Hub
+repo_id = "Web4/LS-W4-Mini-RF_Addiction_Impact"
+model_file = "LS-W4-Mini-RF_Addiction_Impact.joblib"
+
+# Get the Hugging Face token from environment variables
+token = os.environ.get("HF_TOKEN")
+
+# Download the model file from the Hugging Face Hub using the token
+try:
+    model_path = hf_hub_download(repo_id=repo_id, filename=model_file, token=token)
+    print(f"Model downloaded to: {model_path}")
+except Exception as e:
+    print(f"Error downloading model: {e}")
+    raise
+
+# Load the scikit-learn pipeline from the downloaded joblib file
+pipeline = joblib.load(model_path)
+
+# Define the prediction function for the Gradio interface
+def predict_impact(
+    gender,
+    academic_level,
+    most_used_platform,
+    relationship_status,
+    age,
+    avg_daily_usage_hours,
+    sleep_hours_per_night,
+    mental_health_score,
+    addicted_score,
+    conflicts_over_social_media
+):
+    # Create a pandas DataFrame from the user inputs
+    input_data = pd.DataFrame({
+        'Gender': [gender],
+        'Academic_Level': [academic_level],
+        'Most_Used_Platform': [most_used_platform],
+        'Relationship_Status': [relationship_status],
+        'Age': [age],
+        'Avg_Daily_Usage_Hours': [avg_daily_usage_hours],
+        'Sleep_Hours_Per_Night': [sleep_hours_per_night],
+        'Mental_Health_Score': [mental_health_score],
+        'Addicted_Score': [addicted_score],
+        'Conflicts_Over_Social_Media': [conflicts_over_social_media]
+    })
+    
+    # Make a prediction using the loaded pipeline
+    prediction = pipeline.predict(input_data)[0]
+    
+    # Return a user-friendly result based on the prediction
+    if prediction == 1:
+        return "Prediction: Yes, social media use is likely to impact academic performance."
+    else:
+        return "Prediction: No, social media use is likely not to impact academic performance."
+
+# Define the Gradio interface components
+demo = gr.Interface(
+    fn=predict_impact,
+    inputs=[
+        gr.Dropdown(["Male", "Female"], label="Gender"),
+        gr.Dropdown(["Undergraduate", "Postgraduate", "High School"], label="Academic_Level"),
+        gr.Dropdown(["Instagram", "Facebook", "Twitter", "YouTube", "WhatsApp", "Other"], label="Most_Used_Platform"),
+        gr.Dropdown(["Single", "In a relationship"], label="Relationship_Status"),
+        gr.Slider(16, 25, value=20, label="Age"),
+        gr.Slider(0, 24, value=3.0, label="Avg_Daily_Usage_Hours"),
+        gr.Slider(0, 12, value=7, label="Sleep_Hours_Per_Night"),
+        gr.Slider(0, 10, value=5, label="Mental_Health_Score (0-10)"),
+        gr.Slider(0, 10, value=5, label="Addicted_Score (0-10)"),
+        gr.Dropdown([0, 1], label="Conflicts_Over_Social_Media (0=No, 1=Yes)")
+    ],
+    outputs="text",
+    title="Social Media Addiction Impact on Academic Performance",
+    description="A Random Forest model to predict if social media use impacts a student's academic performance."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    demo.launch()