Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| import openslide | |
| import os | |
| from streamlit_option_menu import option_menu | |
| import torch | |
| def load_model(): | |
| from predict import Predictor | |
| predictor = Predictor() | |
| return predictor | |
| #@st.cache(suppress_st_warning=True) | |
| #def load_dependencies(): | |
| # os.system("pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.1+cpu.html") | |
| # os.system("pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.1+cpu.html") | |
| # os.system("pip install torch-geometric -f https://pytorch-geometric.com/whl/torch-1.7.1+cpu.html") | |
| def main(): | |
| os.system("pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.1+cpu.html") | |
| os.system("pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.1+cpu.html") | |
| os.system("pip install torch-geometric -f https://pytorch-geometric.com/whl/torch-1.7.1+cpu.html") | |
| # environment variables for the inference api | |
| os.environ['DATA_DIR'] = 'queries' | |
| os.environ['PATCHES_DIR'] = os.path.join(os.environ['DATA_DIR'], 'patches') | |
| os.environ['SLIDES_DIR'] = os.path.join(os.environ['DATA_DIR'], 'slides') | |
| os.environ['GRAPHCAM_DIR'] = os.path.join(os.environ['DATA_DIR'], 'graphcam_plots') | |
| os.makedirs(os.environ['GRAPHCAM_DIR'], exist_ok=True) | |
| # manually put the metadata in the metadata folder | |
| os.environ['CLASS_METADATA'] ='metadata/label_map.pkl' | |
| # manually put the desired weights in the weights folder | |
| os.environ['WEIGHTS_PATH'] = WEIGHTS_PATH='weights' | |
| os.environ['FEATURE_EXTRACTOR_WEIGHT_PATH'] = os.path.join(os.environ['WEIGHTS_PATH'], 'feature_extractor', 'model.pth') | |
| os.environ['GT_WEIGHT_PATH'] = os.path.join(os.environ['WEIGHTS_PATH'], 'graph_transformer', 'GraphCAM.pth') | |
| #st.set_page_config(page_title="",layout='wide') | |
| predictor = load_model()#Predictor() | |
| ABOUT_TEXT = "🤗 LastMinute Medical - Web diagnosis tool." | |
| CONTACT_TEXT = """ | |
| _Built by Christian Cancedda and LabLab lads with love_ ❤️ | |
| [](https://github.com/Chris1nexus) | |
| [](https://twitter.com/intent/follow?screen_name=chris_cancedda) | |
| Star project repository: | |
| [](https://github.com/Chris1nexus/inference-graph-transformer) | |
| """ | |
| VISUALIZE_TEXT = "Visualize WSI slide by uploading it on the provided window" | |
| DETECT_TEXT = "Generate a preliminary diagnosis about the presence of pulmonary disease" | |
| with st.sidebar: | |
| choice = option_menu("LastMinute - Diagnosis", | |
| ["About", "Visualize WSI slide", "Cancer Detection", "Contact"], | |
| icons=['house', 'upload', 'activity', 'person lines fill'], | |
| menu_icon="app-indicator", default_index=0, | |
| styles={ | |
| # "container": {"padding": "5!important", "background-color": "#fafafa", }, | |
| "container": {"border-radius": ".0rem"}, | |
| # "icon": {"color": "orange", "font-size": "25px"}, | |
| # "nav-link": {"font-size": "16px", "text-align": "left", "margin": "0px", | |
| # "--hover-color": "#eee"}, | |
| # "nav-link-selected": {"background-color": "#02ab21"}, | |
| } | |
| ) | |
| st.sidebar.markdown( | |
| """ | |
| <style> | |
| .aligncenter { | |
| text-align: center; | |
| } | |
| </style> | |
| <p style='text-align: center'> | |
| <a href="https://github.com/Chris1nexus/inference-graph-transformer" target="_blank">Project Repository</a> | |
| </p> | |
| <p class="aligncenter"> | |
| <a href="https://github.com/Chris1nexus/inference-graph-transformer" target="_blank"> | |
| <img src="https://img.shields.io/github/stars/Chris1nexus/inference-graph-transformer?style=social"/> | |
| </a> | |
| </p> | |
| <p class="aligncenter"> | |
| <a href="https://twitter.com/chris_cancedda" target="_blank"> | |
| <img src="https://img.shields.io/twitter/follow/chris_cancedda?style=social"/> | |
| </a> | |
| </p> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| if choice == "About": | |
| st.title(choice) | |
| README = requests.get("https://raw.githubusercontent.com/Chris1nexus/inference-graph-transformer/master/README.md").text | |
| README = str(README).replace('width="1200"','width="700"') | |
| # st.title(choose) | |
| st.markdown(README, unsafe_allow_html=True) | |
| if choice == "Visualize WSI slide": | |
| st.title(choice) | |
| st.markdown(VISUALIZE_TEXT) | |
| uploaded_file = st.file_uploader("Choose a WSI slide file to diagnose (.svs)") | |
| if uploaded_file is not None: | |
| ori = openslide.OpenSlide(uploaded_file.name) | |
| width, height = ori.dimensions | |
| REDUCTION_FACTOR = 20 | |
| w, h = int(width/512), int(height/512) | |
| w_r, h_r = int(width/20), int(height/20) | |
| resized_img = ori.get_thumbnail((w_r,h_r)) | |
| resized_img = resized_img.resize((w_r,h_r)) | |
| ratio_w, ratio_h = width/resized_img.width, height/resized_img.height | |
| #print('ratios ', ratio_w, ratio_h) | |
| w_s, h_s = float(512/REDUCTION_FACTOR), float(512/REDUCTION_FACTOR) | |
| st.image(resized_img, use_column_width='never') | |
| if choice == "Cancer Detection": | |
| state = dict() | |
| st.title(choice) | |
| st.markdown(DETECT_TEXT) | |
| uploaded_file = st.file_uploader("Choose a WSI slide file to diagnose (.svs)") | |
| st.markdown("Examples can be chosen at the [GDC Data repository](https://portal.gdc.cancer.gov/repository?facetTab=cases&filters=%7B%22op%22%3A%22and%22%2C%22content%22%3A%5B%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22cases.primary_site%22%2C%22value%22%3A%5B%22bronchus%20and%20lung%22%5D%7D%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22cases.project.program.name%22%2C%22value%22%3A%5B%22TCGA%22%5D%7D%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22cases.project.project_id%22%2C%22value%22%3A%5B%22TCGA-LUAD%22%2C%22TCGA-LUSC%22%5D%7D%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22files.experimental_strategy%22%2C%22value%22%3A%5B%22Tissue%20Slide%22%5D%7D%7D%5D%7D)") | |
| st.markdown("Alternatively, for simplicity few test cases are provided at the [drive link](https://drive.google.com/drive/folders/1u3SQa2dytZBHHh6eXTlMKY-pZGZ-pwkk?usp=share_link)") | |
| if uploaded_file is not None: | |
| # To read file as bytes: | |
| #print(uploaded_file) | |
| with open(os.path.join(uploaded_file.name),"wb") as f: | |
| f.write(uploaded_file.getbuffer()) | |
| with st.spinner(text="Computation is running"): | |
| predicted_class, viz_dict = predictor.predict(uploaded_file.name) | |
| st.info('Computation completed.') | |
| st.header(f'Predicted to be: {predicted_class}') | |
| st.text('Heatmap of the areas that show markers correlated with the disease.\nIncreasing red tones represent higher likelihood that the area is affected') | |
| state['cur'] = predicted_class | |
| mapper = {'ORI': predicted_class, predicted_class:'ORI'} | |
| readable_mapper = {'ORI': 'Original', predicted_class :'Disease heatmap' } | |
| #def fn(): | |
| # st.image(viz_dict[mapper[state['cur']]], use_column_width='never', channels='BGR') | |
| # state['cur'] = mapper[state['cur']] | |
| # return | |
| #st.button(f'See {readable_mapper[mapper[state["cur"]] ]}', on_click=fn ) | |
| #st.image(viz_dict[state['cur']], use_column_width='never', channels='BGR') | |
| st.image([viz_dict[state['cur']],viz_dict['ORI']], caption=['Original', f'{predicted_class} heatmap'] ,channels='BGR' | |
| # use_column_width='never', | |
| ) | |
| if choice == "Contact": | |
| st.title(choice) | |
| st.markdown(CONTACT_TEXT) | |
| if __name__ == '__main__': | |
| #''' | |
| #load_dependencies() | |
| #''' | |
| main() | |