Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Load dataset (Pima Indians Diabetes dataset)
+url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
+columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome']
+data = pd.read_csv(url, names=columns)
+
+# Split data into features and target
+X = data.drop('Outcome', axis=1)
+y = data['Outcome']
+
+# Train-Test Split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Create and train RandomForestClassifier
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Check model accuracy
+y_pred = model.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Model Accuracy: {accuracy * 100:.2f}%")
+
+# Function for prediction with debugging
+def predict_diabetes(Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age):
+    input_data = [[Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age]]
+    prediction = model.predict(input_data)
+    return "Diabetic" if prediction[0] == 1 else "Not Diabetic"
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Diabetes Prediction App")
+    
+    with gr.Row():
+        pregnancies = gr.Number(label="Pregnancies", value=0, precision=0)
+        glucose = gr.Number(label="Glucose", value=120)
+        blood_pressure = gr.Number(label="Blood Pressure", value=70)
+        skin_thickness = gr.Number(label="Skin Thickness", value=20)
+    
+    with gr.Row():
+        insulin = gr.Number(label="Insulin", value=80)
+        bmi = gr.Number(label="BMI", value=30.0)
+        pedigree = gr.Number(label="Diabetes Pedigree Function", value=0.5)
+        age = gr.Number(label="Age", value=30, precision=0)
+    
+    predict_button = gr.Button("Predict")
+    output = gr.Textbox(label="Prediction", placeholder="Prediction will appear here.")
+
+    # Connect button to prediction function
+    predict_button.click(
+        fn=predict_diabetes, 
+        inputs=[pregnancies, glucose, blood_pressure, skin_thickness, insulin, bmi, pedigree, age], 
+        outputs=output
+    )
+
+# Launch the Gradio app
+demo.launch()