Update app

app.py

@@ -5,141 +5,157 @@ from streamlit_option_menu import option_menu
 import torch
 
 
-if torch.cuda.is_available():
-    os.system("pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.1+cu113.html")
-    os.system("pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.1+cu113.html")
-    os.system("pip install torch-geometric -f https://pytorch-geometric.com/whl/torch-1.7.1+cu113.html")    
-else:
-    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")
-
-from predict import Predictor
-
-
-
-# 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 = Predictor()
-
-
-
-
-
-ABOUT_TEXT = "🤗 LastMinute Medical - Web diagnosis tool."
-CONTACT_TEXT = """
-_Built by Christian Cancedda and LabLab lads with love_ ❤️ 
-[![Follow](https://img.shields.io/github/followers/Chris1nexus?style=social)](https://github.com/Chris1nexus)
-[![Follow](https://img.shields.io/twitter/follow/chris_cancedda?style=social)](https://twitter.com/intent/follow?screen_name=chris_cancedda)
-"""
-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 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)
-
-
-
-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)")
-    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)
+def main():
+
+        from predict import Predictor
+
+
+
+        # 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 = Predictor()
+
+
+
+
+
+        ABOUT_TEXT = "🤗 LastMinute Medical - Web diagnosis tool."
+        CONTACT_TEXT = """
+        _Built by Christian Cancedda and LabLab lads with love_ ❤️ 
+        [![Follow](https://img.shields.io/github/followers/Chris1nexus?style=social)](https://github.com/Chris1nexus)
+        [![Follow](https://img.shields.io/twitter/follow/chris_cancedda?style=social)](https://twitter.com/intent/follow?screen_name=chris_cancedda)
+        Star project repository:
+        [![GitHub stars](https://img.shields.io/github/followers/Chris1nexus?style=social)](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)
+
+
+
+        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)")
+            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__':
+    if torch.cuda.is_available():
+        os.system("pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.1+cu113.html")
+        os.system("pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.1+cu113.html")
+        os.system("pip install torch-geometric -f https://pytorch-geometric.com/whl/torch-1.7.1+cu113.html")    
+    else:
+        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")
+    main()