Update app.py

app.py

@@ -2,40 +2,47 @@ import gradio as gr
 import pandas as pd
 import pickle
 import numpy as np
-import numpy as np
 import pandas as pd
 
-
-
-# Load your ML model (replace 'model.pkl' with your model file)
+# Load the model
 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):
+# Define the prediction function
+def predict_diabetes_risk(pregnancies, glucose, blood_pressure, insulin, bmi, age):
     """
-    Predict health risk based on user input.
+    Predict diabetes risk based on user inputs.
     """
-    input_data = np.array([[age, activity_level, diet_score, health_history]])
-    prediction = model.predict(input_data)
-    return f"Predicted Risk: {prediction[0]}"
+    # Prepare input data
+    input_data = np.array([[pregnancies, glucose, blood_pressure, insulin, bmi, age]])
+    prediction = model.predict(input_data)  # Assuming the model has a `predict` method
+    probability = model.predict_proba(input_data)[0][1]  # If available, get prediction probability
+    risk_level = "High Risk" if prediction[0] == 1 else "Low Risk"
+    return f"Risk Level: {risk_level} (Probability: {probability:.2%})"
 
-# Set up Gradio interface
+# Set up the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("## Personalized Health Tracker")
-    gr.Markdown("Enter your details to predict health risks.")
-    
+    gr.Markdown("## Diabetes Risk Predictor")
+    gr.Markdown("Enter the following details to predict your risk of diabetes.")
+
     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)
+        pregnancies = gr.Number(label="Pregnancies", value=0)
+        glucose = gr.Number(label="Glucose Level", value=0)
+        blood_pressure = gr.Number(label="Blood Pressure Level", value=0)
+        insulin = gr.Number(label="Insulin Level", value=0)
+        bmi = gr.Number(label="BMI (Body Mass Index)", value=0)
+        age = gr.Number(label="Age", value=0)
+
+    predict_button = gr.Button("Predict Risk")
+    output = gr.Textbox(label="Prediction Result")
+
+    predict_button.click(
+        predict_diabetes_risk,
+        inputs=[pregnancies, glucose, blood_pressure, insulin, bmi, age],
+        outputs=output,
+    )
 
 # Launch the app
 if __name__ == "__main__":
     demo.launch()
+