Create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+import pandas as pd
+import pickle
+import numpy as np
+
+# Load your ML model (replace 'model.pkl' with your model file)
+with open("model.pkl", "rb") as model_file:
+    model = pickle.load(model_file)
+
+# Define your prediction function
+def predict_health_risk(age, activity_level, diet_score, health_history):
+    """
+    Predict health risk based on user input.
+    """
+    input_data = np.array([[age, activity_level, diet_score, health_history]])
+    prediction = model.predict(input_data)
+    return f"Predicted Risk: {prediction[0]}"
+
+# Set up Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Personalized Health Tracker")
+    gr.Markdown("Enter your details to predict health risks.")
+    
+    with gr.Row():
+        age = gr.Number(label="Age", value=30)
+        activity_level = gr.Number(label="Activity Level (1-10)", value=5)
+        diet_score = gr.Number(label="Diet Score (1-10)", value=5)
+        health_history = gr.Number(label="Health History (e.g., 1 for Yes, 0 for No)", value=0)
+    
+    predict_btn = gr.Button("Predict Risk")
+    output = gr.Textbox(label="Risk Score")
+    
+    predict_btn.click(predict_health_risk, inputs=[age, activity_level, diet_score, health_history], outputs=output)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()