Create app.py

app.py

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+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+import gradio as gr
+
+# Load dataset
+url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
+column_names = ["Pregnancies", "Glucose", "BloodPressure", "SkinThickness", "Insulin", 
+                "BMI", "DiabetesPedigreeFunction", "Age", "Outcome"]
+df = pd.read_csv(url, header=None, names=column_names)
+
+# Replace zero values with mean
+cols_with_zero = ["Glucose", "BloodPressure", "SkinThickness", "Insulin", "BMI"]
+df[cols_with_zero] = df[cols_with_zero].replace(0, np.nan)
+df.fillna(df.mean(), inplace=True)
+
+# Feature selection
+selected_features = ["Pregnancies", "Glucose", "Insulin", "BMI", "Age"]
+X = df[selected_features]
+y = df["Outcome"]
+
+# Normalize
+scaler = MinMaxScaler()
+X_scaled = scaler.fit_transform(X)
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
+
+# Build ANN model
+model = Sequential([
+    Dense(12, activation='relu', input_shape=(X_train.shape[1],)),
+    Dense(8, activation='relu'),
+    Dense(1, activation='sigmoid')
+])
+
+# Compile and train
+model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),
+              loss='binary_crossentropy',
+              metrics=['accuracy'])
+model.fit(X_train, y_train, epochs=400, batch_size=16, verbose=0)
+
+# Prediction function
+def predict_diabetes(pregnancies, glucose, insulin, bmi, age):
+    input_data = np.array([[pregnancies, glucose, insulin, bmi, age]])
+    input_scaled = scaler.transform(input_data)
+    prediction = model.predict(input_scaled)[0][0]
+    return "Diabetic" if prediction >= 0.5 else "Not Diabetic"
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_diabetes,
+    inputs=[
+        gr.Number(label="Pregnancies"),
+        gr.Number(label="Glucose"),
+        gr.Number(label="Insulin"),
+        gr.Number(label="BMI"),
+        gr.Number(label="Age")
+    ],
+    outputs="text",
+    title="Diabetes Prediction using ANN",
+    description="Enter medical values to predict whether a person has diabetes"
+)
+
+iface.launch()