Create app.py

app.py

@@ -0,0 +1,65 @@
+import joblib
+import gradio as gr
+
+# Load the saved model
+gbc = joblib.load('diabetes_model.pkl')  
+
+def prediction(a, b, c, d, e, f, g, h):
+    try:
+        # Create a DataFrame for the new input
+        new_data = [[a, b, c, d, e, f, g, h]]
+
+        # Make a prediction
+        result = gbc.predict(new_data)
+
+        # Print the inputs
+        input_data = {
+            "Pregnancies": a,
+            "Glucose": b,
+            "BloodPressure": c,
+            "SkinThickness": d,
+            "Insulin": e,
+            "BMI": f,
+            "DiabetesPedigreeFunction": g,
+            "Age": h,
+        }
+
+        print("User Input:", input_data)  # Print inputs to the console
+
+        # Determine the prediction message
+        if result[0] == 0:
+            prediction_message = "Based on your results, you are not diabetic. Maintain a healthy lifestyle and regular check-ups."
+        else:
+            prediction_message = "Your results indicate that you are diabetic. Please consult with a healthcare professional for further guidance and management."
+
+        # Return both the prediction and the image URL
+        return prediction_message, image_url
+    except Exception as e:
+        return f"Error: {str(e)}", None  # Return None for the image in case of error
+
+# Example values
+example_inputs = [6, 148.0, 72.0, 35.0, 79.799479, 33.6, 0.627, 50]
+
+# Image URL (replace this with a direct image URL)
+image_url = "pngtree-world-diabetes-day-raising-awareness-for-a-healthier-future-png-image_14138135.png"
+
+# Create the Gradio interface with a soft theme
+app = gr.Interface(
+    fn=prediction,
+    inputs=[
+        gr.Number(label="Pregnancies", value=example_inputs[0]),
+        gr.Number(label="Glucose", value=example_inputs[1]),
+        gr.Number(label="BloodPressure", value=example_inputs[2]),
+        gr.Number(label="SkinThickness", value=example_inputs[3]),
+        gr.Number(label="Insulin", value=example_inputs[4]),
+        gr.Number(label="BMI", value=example_inputs[5]),
+        gr.Number(label="DiabetesPedigreeFunction", value=example_inputs[6]),
+        gr.Number(label="Age", value=example_inputs[7])
+    ],
+    outputs=[gr.Text(label="Prediction"), gr.Image(value=image_url, label="Image", show_label=True)],
+    title="Diabetes Prediction",
+    theme=gr.themes.Soft()
+)
+
+# Launch the app
+app.launch()