app.py

import streamlit as st
import gradio as gr
import cv2
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.preprocessing import image

# Loading Models
braintumor_model = load_model('models/brain_tumor_binary.h5')

# Configuring Streamlit
st.set_page_config(page_title="Brain Tumor Prediction App", page_icon=":brain:")

def preprocess_imgs(set_name, img_size):
    set_new = []
    for img in set_name:
        img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC)
        set_new.append(preprocess_input(img))
    return np.array(set_new)

# Handle binary decision
def binary_decision(confidence):
    return 1 if confidence >= 0.5 else 0

def predict_braintumor(img):
    # If it's a NumPy array, use it directly
    if isinstance(img, np.ndarray):
        img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    else:
        # Convert Gradio image data to bytes
        img_bytes = img.read()

        # Convert to NumPy array
        nparr = np.frombuffer(img_bytes, np.uint8)

        # Decode image
        img_gray = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)

    # Crop and preprocess the grayscale image
    img_processed = preprocess_imgs([img_gray], (224, 224))

    # Make prediction
    pred = braintumor_model.predict(img_processed)

    # Handle binary decision
    confidence = pred[0][0]
    return "Brain Tumor Not Found!" if binary_decision(confidence) == 1 else "Brain Tumor Found!"

def main():
    st.title("Brain Tumor Prediction App")

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

    if uploaded_file is not None:
        st.image(uploaded_file, caption="Uploaded Image.", use_column_width=True)
        st.write("")
        st.write("Classifying...")

        # Make prediction
        result = predict_braintumor(uploaded_file)

        # Display prediction
        st.subheader("Prediction:")
        st.success(result)

if __name__ == "__main__":
    # Streamlit app
    main()

    # Gradio interface
    iface = gr.Interface(
        fn=predict_braintumor,
        inputs="image",
        outputs="text",
        examples=[["examples/1_no.jpeg"], ["examples/2_no.jpeg"], ["examples/3_no.jpeg"], ["examples/Y1.jpg"], ["examples/Y2.jpeg"], ["examples/Y3.jpg"]]
    )
    iface.launch()