Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| from PIL import Image | |
| import numpy as np | |
| import torch | |
| from transformers import ViTForImageClassification | |
| # Load the Hugging Face model for ECG classification | |
| model_name = "adilsaleem/ecg-image-multilabel-classification" | |
| try: | |
| model = ViTForImageClassification.from_pretrained(model_name) | |
| model.eval() # Set the model to evaluation mode | |
| except Exception as e: | |
| st.error(f"Error loading model: {str(e)}") | |
| st.stop() | |
| # CSS for styling | |
| st.markdown( | |
| """ | |
| <style> | |
| body { | |
| background-color: #F5F7FA; /* Light background color */ | |
| font-family: 'Arial', sans-serif; | |
| display: flex; | |
| flex-direction: column; | |
| height: 100vh; | |
| margin: 0; | |
| } | |
| .title { | |
| font-size: 40px; | |
| text-align: center; | |
| color: #2980B9; /* Beautiful blue color */ | |
| font-weight: bold; /* Bold text */ | |
| margin: 20px 0; | |
| } | |
| .subtitle { | |
| font-size: 20px; | |
| text-align: center; | |
| color: #34495E; /* Darker shade for subtitle */ | |
| margin-bottom: 20px; | |
| } | |
| .footer { | |
| background-color: #2C3E50; /* Darker blue for footer */ | |
| color: white; /* Text color in footer */ | |
| text-align: center; | |
| padding: 15px; /* Padding for footer */ | |
| position: sticky; | |
| bottom: 0; | |
| width: 100%; | |
| font-size: 14px; /* Font size for footer */ | |
| } | |
| .about { | |
| margin: 20px 0; | |
| text-align: center; | |
| color: #34495E; /* Darker shade for about section */ | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| # App Title | |
| st.markdown('<div class="title">ECG READER</div>', unsafe_allow_html=True) | |
| # About Us Section in Expander | |
| with st.expander("About the App", expanded=True): | |
| st.write(""" | |
| This ECG Reader application uses AI to analyze ECG images and provide insights into heart health. | |
| It helps users determine whether their ECG results are clear or unclear. | |
| Developed by Hamaad Ayub Khan. | |
| """) | |
| st.write("Connect with me:") | |
| st.markdown('- [Instagram](https://www.instagram.com/hamaadayubkhan)', unsafe_allow_html=True) | |
| st.markdown('- [GitHub](https://github.com/hakgs1234)', unsafe_allow_html=True) | |
| st.markdown('- [My Email](mailto:hakgss1234@gmail.com)', unsafe_allow_html=True) | |
| st.markdown('- [Facebook](https://www.facebook.com/hamaadayubkhan)', unsafe_allow_html=True) | |
| def preprocess_image(image): | |
| try: | |
| image = image.convert("RGB") | |
| image = image.resize((224, 224)) | |
| image_array = np.array(image) / 255.0 | |
| image_array = np.transpose(image_array, (2, 0, 1)) | |
| return torch.tensor(image_array).unsqueeze(0) | |
| except Exception as e: | |
| st.error(f"Error processing image: {str(e)}") | |
| return None | |
| def classify_ecg(image): | |
| processed_image = preprocess_image(image) | |
| if processed_image is None: | |
| return "Error in image processing." | |
| try: | |
| with torch.no_grad(): | |
| outputs = model(processed_image) | |
| logits = outputs.logits | |
| predicted_class = torch.argmax(logits, dim=1).item() | |
| return "Clear" if predicted_class == 1 else "Unclear" | |
| except Exception as e: | |
| st.error(f"Error during model inference: {str(e)}") | |
| return "Error in classification." | |
| def get_ecg_explanation(status): | |
| explanations = { | |
| "Clear": "A clear ECG indicates normal heart activity without significant issues.", | |
| "Unclear": "An unclear ECG may indicate possible abnormalities; further evaluation is recommended." | |
| } | |
| return explanations.get(status, "No explanation available.") | |
| uploaded_file = st.file_uploader("Select an ECG image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file: | |
| try: | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption="Uploaded ECG Image.", use_column_width=True) | |
| # Make predictions using the Hugging Face model | |
| heart_status = classify_ecg(image) | |
| st.write(f"Predicted heart status: {heart_status}") | |
| # Get explanation based on the status | |
| explanation = get_ecg_explanation(heart_status) | |
| st.write(f"Explanation: {explanation}") | |
| except Exception as e: | |
| st.error(f"Error uploading or displaying image: {str(e)}") | |
| # Footer | |
| st.markdown( | |
| """ | |
| <div class="footer"> | |
| <p>AI can make mistakes; please consult your doctor.</p> | |
| <p>ECG READER All rights reserved.</p> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |