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| 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 | |
| # Loading Models | |
| braintumor_model = load_model('models/brain_tumor_binary.h5') | |
| # Configure Streamlit | |
| st.set_page_config( | |
| page_title="Brain Tumor Detection", | |
| page_icon=":brain:", | |
| layout="wide", | |
| ) | |
| # Title and description | |
| title = "Brain Tumor Detection App" | |
| description = gr.Markdown( | |
| """Upload an MRI image in JPG or | |
| PNG format, and discover whether it shows signs of a brain tumor. | |
| This is Dataset of the Brain Tumor Classifier: | |
| [Kaggle Dataset](https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection/) | |
| """).value | |
| # Function to preprocess the image | |
| def preprocess_image(img): | |
| if isinstance(img, np.ndarray): | |
| img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
| else: | |
| img_bytes = img.read() | |
| nparr = np.frombuffer(img_bytes, np.uint8) | |
| img_gray = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE) | |
| img_processed = preprocess_imgs([img_gray], (224, 224)) | |
| return img_processed | |
| # Handle binary decision | |
| def binary_decision(confidence): | |
| return 1 if confidence >= 0.5 else 0 | |
| def predict_braintumor(img): | |
| img_processed = preprocess_image(img) | |
| pred = braintumor_model.predict(img_processed) | |
| confidence = pred[0][0] | |
| return "Brain Tumor Not Found!" if binary_decision(confidence) == 1 else "Brain Tumor Found!" | |
| 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) | |
| # Streamlit components | |
| uploaded_file = st.file_uploader("Choose an MRI image", type=["jpg", "jpeg"]) | |
| if uploaded_file is not None: | |
| # Display the uploaded image | |
| st.image(uploaded_file, caption="Uploaded MRI Image.", use_column_width=True) | |
| # Perform prediction when the "Predict" button is clicked | |
| if st.button("Predict"): | |
| img_array = preprocess_image(uploaded_file) | |
| pred = braintumor_model.predict(img_array) | |
| confidence = pred[0][0] | |
| result = "Brain Tumor Not Found!" if binary_decision(confidence) == 1 else "Brain Tumor Found!" | |
| # Display the prediction result with confidence | |
| st.success(result) | |
| st.markdown(f"Confidence: {confidence:.2%}") | |
| # Gradio interface | |
| iface = gr.Interface( | |
| title = title, | |
| fn=predict_braintumor, | |
| inputs="image", | |
| outputs="text", | |
| examples=[ | |
| ["examples/1_no.jpeg"], | |
| ["examples/2_no.jpeg"], | |
| ["examples/3_no.jpg"], | |
| ["examples/Y1.jpg"], | |
| ["examples/Y2.jpg"], | |
| ["examples/Y3.jpg"], | |
| ], | |
| description = description, | |
| live=True | |
| ) | |
| # Display Gradio interface | |
| st.markdown("<h1 style='text-align: center;'>Gradio Interface</h1>", unsafe_allow_html=True) | |
| st.markdown( | |
| "<p style='text-align: center;'>This is an interactive interface powered by Gradio.</p>", | |
| unsafe_allow_html=True | |
| ) | |
| st.markdown("<hr>", unsafe_allow_html=True) | |
| # Display Gradio interface | |
| st.markdown("<h2 style='text-align: center;'>Gradio Interface</h2>", unsafe_allow_html=True) | |
| st.markdown( | |
| "<p style='text-align: center;'>This is an interactive interface powered by Gradio.</p>", | |
| unsafe_allow_html=True | |
| ) | |
| iface.launch() | |