first commit

Procfile

@@ -0,0 +1 @@
+web: sh setup.sh && streamlit run app.py

README.md

@@ -0,0 +1,12 @@
+---
+title: Lineage Population Model
+emoji: 🐨
+colorFrom: purple
+colorTo: green
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,18 @@
+import functions
+import streamlit as st
+import numpy as np
+import pandas as pd
+from PIL import Image
+from pathlib import Path
+import joblib
+
+import numpy as np
+import cv2
+import onnxruntime as ort
+import imutils
+# import matplotlib.pyplot as plt
+import pandas as pd
+import plotly.express as px
+
+
+functions.lineage_population_model()

functions.py

@@ -0,0 +1,85 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+from PIL import Image
+from pathlib import Path
+import joblib
+
+import numpy as np
+import cv2
+import onnxruntime as ort
+import imutils
+# import matplotlib.pyplot as plt
+import pandas as pd
+import plotly.express as px
+
+
+def scale_model_outputs(scaler_path, data):
+    scaler= joblib.load(scaler_path)
+    scaled=scaler.inverse_transform(data)
+    return(scaled)
+
+
+def onnx_predict_lineage_population(input_image):
+    ort_session = ort.InferenceSession('onnx_models/lineage_population_model.onnx')
+    img = Image.fromarray(np.uint8(input_image))
+    resized = img.resize((256, 256), Image.NEAREST)
+
+    transposed=np.transpose(resized, (2, 1, 0))  
+    img_unsqueeze = expand_dims(transposed)
+
+    onnx_outputs = ort_session.run(None, {'input': img_unsqueeze.astype('float32')}) 
+    return(onnx_outputs[0])
+
+
+
+def expand_dims(arr):
+    norm=(arr-np.min(arr))/(np.max(arr)-np.min(arr)) #normalize
+    ret = np.expand_dims(norm, axis=0)
+    return(ret)
+
+
+
+def lineage_population_model():
+    selected_box2 = st.sidebar.selectbox(
+    'Choose Example Input',
+    (['Example_1.png'])
+    )
+
+    st.title('Predict Cell Lineage Populations')
+    instructions = """
+        Predict the population of cells in C. elegans embryo using fluorescence microscopy data. \n
+        Either upload your own image or select from the sidebar to get a preconfigured image. 
+        The image you select or upload will be fed through the Deep Neural Network in real-time 
+        and the output will be displayed to the screen.
+        """
+    st.text(instructions)
+    file = st.file_uploader('Upload an image or choose an example')
+    example_image = Image.open('./images/lineage_population_examples/'+selected_box2).convert("RGB")
+
+    col1, col2= st.beta_columns(2)
+
+    if file:
+        input = Image.open(file).convert("RGB")
+        fig1 = px.imshow(input, binary_string=True, labels=dict(x="Input Image"))
+        fig1.update(layout_coloraxis_showscale=False)
+        fig1.update_layout(margin=dict(l=0, r=0, b=0, t=0))
+        col1.plotly_chart(fig1, use_container_width=True)
+    else:
+        input = example_image
+        fig1 = px.imshow(input, binary_string=True, labels=dict(x="Input Image"))
+        fig1.update(layout_coloraxis_showscale=False)
+        fig1.update_layout(margin=dict(l=0, r=0, b=0, t=0))
+        col1.plotly_chart(fig1, use_container_width=True)
+
+    pressed = st.button('Run')
+    if pressed:
+        st.empty()
+        output = onnx_predict_lineage_population(np.array(input))
+        scaled_output = scale_model_outputs(scaler_path="./scaler.gz", data=output)
+
+        for i in range(len(scaled_output[0])):
+            scaled_output[0][i]=int(round(scaled_output[0][i]))
+
+        df = pd.DataFrame({"Lineage":["A", "E", "M", "P", "C", "D", "Z"] , "Population": scaled_output[0]})
+        col2.table(df)

requirements.txt

@@ -0,0 +1,89 @@
+altair==4.1.0
+argon2-cffi==20.1.0
+astor==0.8.1
+async-generator==1.10
+attrs==21.2.0
+backcall==0.2.0
+base58==2.1.0
+bleach==3.3.1
+blinker==1.4
+cachetools==4.2.2
+certifi==2021.5.30
+cffi==1.14.6
+charset-normalizer==2.0.3
+click==7.1.2
+debugpy==1.4.1
+decorator==5.0.9
+defusedxml==0.7.1
+entrypoints==0.3
+flatbuffers==2.0
+gitdb==4.0.7
+GitPython==3.1.18
+idna==3.2
+imutils==0.5.4
+ipykernel==6.0.3
+ipython==7.25.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+Jinja2==3.0.1
+joblib==1.0.1
+jsonschema==3.2.0
+jupyter-client==6.1.12
+jupyter-core==4.7.1
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+MarkupSafe==2.0.1
+matplotlib-inline==0.1.2
+mistune==0.8.4
+nbclient==0.5.3
+nbconvert==6.1.0
+nbformat==5.1.3
+nest-asyncio==1.5.1
+notebook==6.4.0
+numpy==1.21.1
+onnxruntime==1.8.1
+opencv-python-headless==4.5.3.56
+packaging==21.0
+pandas==1.3.1
+pandocfilters==1.4.3
+parso==0.8.2
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==8.3.1
+plotly==5.1.0
+prometheus-client==0.11.0
+prompt-toolkit==3.0.19
+protobuf==3.17.3
+ptyprocess==0.7.0
+pyarrow==5.0.0
+pycparser==2.20
+pydeck==0.6.2
+Pygments==2.9.0
+pyparsing==2.4.7
+pyrsistent==0.18.0
+python-dateutil==2.8.2
+pytz==2021.1
+pyzmq==22.1.0
+requests==2.26.0
+scikit-learn==0.24.1
+scipy==1.7.0
+Send2Trash==1.7.1
+six==1.16.0
+smmap==4.0.0
+streamlit==0.85.1
+tenacity==8.0.1
+terminado==0.10.1
+testpath==0.5.0
+threadpoolctl==2.2.0
+toml==0.10.2
+toolz==0.11.1
+tornado==6.1
+traitlets==5.0.5
+tzlocal==2.1
+urllib3==1.26.6
+validators==0.18.2
+watchdog==2.1.3
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.5.1

setup.sh

@@ -0,0 +1,8 @@
+mkdir -p ~/.streamlit/
+echo "\
+[server]\n\
+headless = true\n\
+port = $PORT\n\
+enableCORS = false\n\
+\n\
+" > ~/.streamlit/config.toml