Prediction.py

import streamlit as st
from tensorflow.keras.models import load_model
import numpy as np
import cv2


# Load the Models

model = load_model('cancer_model.h5')
class_names= ['adenocarcinoma', 'large cell carcinoma', 'normal', 'squamous cell carcinoma']

def predict(image):
    # Preprocess the image
    image = cv2.resize(image, (460, 460))
    image = image.astype('float32') / 255.0
    image = image[np.newaxis, :]
    
    # Make predictions
    predictions = model.predict(image)
    
    # Get the predicted class name
    predicted_class_index = np.argmax(predictions[0])
    predicted_class_name = class_names[predicted_class_index]
    
    return predicted_class_name


def app():
    st.title('CNN Image Classifier')

    uploaded_file = st.file_uploader("Choose an image...", type=['jpg', 'jpeg', 'png'])

    if uploaded_file is not None:
        # Read the uploaded image
        image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), 1)

        # Display the uploaded image
        st.image(image, caption='Uploaded Image', use_column_width=True)

        # Make predictions and display the result
        prediction = predict(image)
        st.write('Prediction:', prediction)