app.py

import streamlit as st
import pandas as pd
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation

# Set up the Streamlit app
st.title('Breast Cancer Prediction')

# Default parameter values
default_values = [17.99, 10.38, 122.8, 1001, 0.1184, 0.2776, 0.3001, 0.1471, 0.2419, 0.07871,
                  1.095, 0.9053, 8.589, 153.4, 0.006399, 0.04904, 0.05373, 0.01587, 0.03003,
                  0.006193, 25.38, 17.33, 184.6, 2019, 0.1622, 0.6656, 0.7119, 0.2654, 0.4601, 0.1189]

# Create a DataFrame with default parameter values
default_data = pd.DataFrame([default_values],
                            columns=['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',
                                     'smoothness_mean', 'compactness_mean', 'concavity_mean',
                                     'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
                                     'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
                                     'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
                                     'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst',
                                     'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst',
                                     'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst'])

# Display the input form with default values
st.subheader('Input Parameters')
user_input = st.form(key='user_input_form')
input_data = user_input.dataframe(default_data)

# Implementing ANN
ann_model = Sequential()
ann_model.add(Dense(16, input_dim=30, activation='relu'))
ann_model.add(Dropout(0.2))
ann_model.add(Dense(1, activation='sigmoid'))

# Compiling the model
ann_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Load the saved model weights
ann_model.load_weights('model_weights.h5')

# Make predictions when the 'Predict' button is clicked
if user_input.form_submit_button('Predict'):
    input_array = np.array(input_data)
    prediction = ann_model.predict(input_array)
    prediction_label = 'Malignant' if prediction[0] >= 0.5 else 'Benign'
    st.subheader('Prediction')
    st.write(f'The lesion is predicted to be: {prediction_label}')