Update app.py

app.py

@@ -1,16 +1,23 @@
-#!pip install gradio --upgrade  # Upgrade to the latest version of Gradio
-
+# Import necessary libraries
 import gradio as gr
 import pandas as pd
 import joblib
 from huggingface_hub import hf_hub_download
 
-# Load the model and scaler from Hugging Face Hub
-model_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="logistic_regression_model.joblib")
-scaler_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="scaler.joblib")
+# Function to download model and scaler from Hugging Face Hub
+def download_model():
+    # Download the model and scaler
+    model_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="logistic_regression_model.joblib")
+    scaler_path = hf_hub_download(repo_id="rama0519/DiabeticLogistic123", filename="scaler.joblib")
+    
+    # Load the model and scaler
+    model = joblib.load(model_path)
+    scaler = joblib.load(scaler_path)
+    
+    return model, scaler
 
-model = joblib.load(model_path)
-scaler = joblib.load(scaler_path)
+# Load model and scaler
+model, scaler = download_model()
 
 # Define reasonable ranges for each input parameter
 ranges = {
@@ -49,7 +56,6 @@ def predict_diabetes(pregnancies, glucose, blood_pressure, skin_thickness, insul
     return prediction_text
 
 # Create the Gradio interface
-# Use gr.Slider to allow for setting min and max values
 interface = gr.Interface(
     fn=predict_diabetes,
     inputs=[
@@ -67,5 +73,6 @@ interface = gr.Interface(
     description="Enter the medical details to predict if the patient is diabetic or not."
 )
 
-# Launch the interface
-interface.launch()
+# Launch the Gradio interface
+if __name__ == "__main__":
+    interface.launch()