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()