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import streamlit as st |
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import tensorflow as tf |
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from PIL import Image |
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from tensorflow import keras |
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import numpy as np |
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import os |
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import warnings |
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warnings.filterwarnings("ignore") |
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os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' |
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st.set_page_config( |
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page_title="ChestAI - Pneumonia Detection", |
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page_icon="🫁", |
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initial_sidebar_state="auto", |
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) |
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hide_streamlit_style = """ |
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<style> |
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#MainMenu {visibility: hidden;} |
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footer {visibility: hidden;} |
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</style> |
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""" |
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st.markdown(hide_streamlit_style, unsafe_allow_html=True) |
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class_names = ["Normal", "PNEUMONIA"] |
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with st.sidebar: |
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st.title("ChestAI") |
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st.markdown(""" |
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### About |
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ChestAI uses advanced deep learning to detect pneumonia in chest X-rays. |
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### How to use |
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1. Upload a chest X-ray image (JPG/PNG) |
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2. Wait for the analysis |
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3. View the results and confidence score |
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### Note |
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This tool is for educational purposes only. Always consult healthcare professionals for medical advice. |
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""") |
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st.set_option("deprecation.showfileUploaderEncoding", False) |
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@st.cache_resource(show_spinner=False) |
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def load_model(): |
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try: |
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from huggingface_hub import from_pretrained_keras |
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keras.utils.set_random_seed(42) |
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model = from_pretrained_keras("aaravlovescodes/PneumoniaDetection") |
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return model |
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except Exception as e: |
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st.error(f"Error loading model from Hugging Face: {str(e)}") |
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return None |
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with st.spinner("Model is being loaded..."): |
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model = load_model() |
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if model is None: |
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st.error("Failed to load model from Hugging Face. Please check the model name or path.") |
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st.stop() |
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file = st.file_uploader(" ", type=["jpg", "png"]) |
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def import_and_predict(image_data, model): |
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img_array = keras.preprocessing.image.img_to_array(image_data) |
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img_array = np.expand_dims(img_array, axis=0) |
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img_array = img_array / 255 |
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predictions = model.predict(img_array) |
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return predictions |
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if file is None: |
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st.text("Please upload an image file") |
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else: |
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try: |
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image = keras.preprocessing.image.load_img(file, target_size=(224, 224), color_mode='rgb') |
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st.image(image, caption="Uploaded Image.", use_column_width=True) |
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predictions = import_and_predict(image, model) |
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confidence = float(max(predictions[0]) * 100) |
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prediction_label = class_names[np.argmax(predictions)] |
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st.info(f"Confidence: {confidence:.2f}%") |
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if prediction_label == "Normal": |
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st.balloons() |
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st.success(f"Result: {prediction_label}") |
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else: |
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st.warning(f"Result: {prediction_label}") |
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except Exception as e: |
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st.error(f"Error processing image: {str(e)}") |
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