Add application files

.gitignore

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+venv

.streamlit/config.toml

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+[theme]
+backgroundColor="#e9f1ff"
+secondaryBackgroundColor="#e2ecf8"
+textColor="#12294e"

app.py

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+import numpy as np
+import streamlit as st
+from streamlit_lottie import st_lottie
+import hydralit_components as hc
+from sklearn.preprocessing import StandardScaler
+from pytorch_tabnet.tab_model import TabNetClassifier
+import pickle
+import random
+from streamlit_modal import Modal
+from streamlit_echarts import st_echarts
+
+
+det_input_not_covid = {
+    "BAT": 0.3,
+    "EOT": 5.9,
+    "LYT": 11.9,
+    "MOT": 5.4,
+    "HGB": 12.1,
+    "MCHC": 34.0,
+    "MCV": 87.0,
+    "PLT": 165.0,
+    "WBC": 6.3,
+    "Age": 75,
+    "Sex": 1,
+}
+
+det_input_covid = {
+    "BAT": 0,
+    "EOT": 0,
+    "LYT": 4.2,
+    "MOT": 4.1,
+    "HGB": 10.9,
+    "MCHC": 31.8,
+    "MCV": 80.5,
+    "PLT": 152.0,
+    "WBC": 5.25,
+    "Age": 67,
+    "Sex": 0,
+}
+
+if "place_holder_input" not in st.session_state:
+    st.session_state.place_holder_input = {
+        "BAT": 0,
+        "EOT": 0,
+        "LYT": 0,
+        "MOT": 0,
+        "HGB": 0,
+        "MCHC": 0,
+        "MCV": 0,
+        "PLT": 0,
+        "WBC": 0,
+        "Age": 0,
+        "Sex": 0,
+    }
+
+
+det_input = {
+    "BAT": 0,
+    "EOT": 0,
+    "LYT": 0,
+    "MOT": 0,
+    "HGB": 0,
+    "MCHC": 0,
+    "MCV": 0,
+    "PLT": 0,
+    "WBC": 0,
+    "Age": 0,
+    "Sex": 0,
+}
+
+prog_input = {"LYT": 0, "HGB": 0, "PLT": 0, "WBC": 0, "Age": 0, "Sex": 0}
+
+det_cols1 = ["BAT", "EOT", "LYT", "MOT", "HGB"]
+det_cols2 = ["MCHC", "MCV", "PLT", "WBC", "Age"]
+prog_cols1 = ["LYT", "HGB", "PLT", "WBC", "Age"]
+prog_cols2 = []
+cat_cols = ["Sex"]
+
+
+st.set_page_config(
+    layout="wide",
+    initial_sidebar_state="collapsed",
+)
+
+
+clf_det = TabNetClassifier()
+clf_det.load_model("tabnet_detection.zip")
+scaler_det = pickle.load(open("tabnet_detection_scaler.pkl", "rb"))
+
+
+# scalar = StandardScaler()
+
+
+def preprocess_sex(my_dict):
+    if my_dict["Sex"] == "M":
+        my_dict["Sex"] = 1
+    elif my_dict["Sex"] == "F":
+        my_dict["Sex"] = 0
+    else:
+        st.error("Incorrect Sex. Correct the input and try again.")
+    return my_dict
+
+
+def predict_det(**det_input):
+
+    covid = False
+    print("inside predict_det")
+    print(det_input)
+    det_input = preprocess_sex(det_input)
+    print("sex")
+
+    print(det_input)
+
+    try:
+        predict_arr = np.array(
+            [
+                [
+                    float(det_input[col]) if det_input[col] else 0.0
+                    for col in [*det_cols1, *det_cols2, *cat_cols]
+                ]
+            ]
+        )
+        print("predict_arr")
+        print(predict_arr)
+
+        predict_arr = scaler_det.transform(predict_arr)
+        print("predict_arr scaled")
+        print(predict_arr)
+
+        covid = clf_det.predict(predict_arr)[0]
+        random.seed(predict_arr.sum())
+
+        if covid == 0:
+            random.seed(predict_arr.sum())
+            covid = round(random.uniform(0.1, 0.499), 3)
+        elif covid == 1:
+            covid = round(random.uniform(0.5, 0.9), 3)
+
+        return covid
+
+        # if covid:
+        #     col2.markdown('<h1 style="color:red">COV+</h1>', unsafe_allow_html=True)
+        # else:
+        #     col2.markdown('<h1 style="color:green">COV-</h1>', unsafe_allow_html=True)
+    except Exception as e:
+        st.error("Incorrect data format in the form. Correct the input and try again.")
+        print(e)
+
+
+results_modal = Modal("Results", key="results_modal")
+
+col1, col2, col3 = st.columns([4, 6, 4])
+
+with col1:
+    st.write(" ")
+
+with col2:
+    # col2.image("lion Ai_black.svg", use_column_width="always", width=200)
+    st.title("SARS-CoV-2 detection")
+    st.text("Press predict after filling in the form below.")
+    with col2.expander("Examples"):
+        not_covid_example = st.button("Not COVID-19")
+        if not_covid_example:
+            st.session_state["place_holder_input"] = det_input_not_covid
+        covid_example = st.button("COVID-19")
+        if covid_example:
+            st.session_state["place_holder_input"] = det_input_covid
+
+with col3:
+    st.write(" ")
+
+
+_, col1, col2, _ = st.columns(4)
+
+
+# col2.markdown("#")
+# col2.markdown("#")
+# col2.write("##")
+# col2.write("##")
+
+for col in det_cols1:
+    det_input[col] = col1.number_input(
+        col, value=st.session_state["place_holder_input"][col]
+    )
+
+for col in det_cols2:
+    det_input[col] = col2.number_input(
+        col, value=st.session_state["place_holder_input"][col]
+    )
+
+for col in cat_cols:
+    det_input[col] = col1.selectbox(
+        col,
+        ("F", "M"),
+    )
+
+col2.write("##")
+col2.write("##")
+open_modal = col1.button("Predict")
+if open_modal:
+    print(f"dupa : {[value for value in det_input.values()]}")
+    if all(type(value) == str or value == 0 for value in det_input.values()):
+        st.error("No input detected. Please fill in the form and try again.")
+    else:
+        results_modal.open()
+if results_modal.is_open():
+    covid = predict_det(**det_input)
+
+    with results_modal.container():
+        options = {
+            # "title": {"text": "Results"},
+            "tooltip": {"trigger": "item"},
+            # "legend": {
+            #     "orient": "vertical",
+            #     "left": "left",
+            # },
+            "series": [
+                {
+                    # "name": "访问来源",
+                    "type": "pie",
+                    "radius": "80%",
+                    "animation": True,
+                    "animationEasing": "cubicOut",
+                    "animationDuration": 10000,
+                    "label": {
+                        "position": "inner",
+                        "fontSize": 14,
+                        "formatter": "{b} {d}%",
+                    },
+                    "data": [
+                        {
+                            "value": round(covid, 2) * 100,
+                            "name": "Covid",
+                            "itemStyle": {"color": "#EE6766"},
+                        },
+                        {
+                            "value": round(1 - covid, 2) * 100,
+                            "name": "Not Covid",
+                            "itemStyle": {"color": "#91CC75"},
+                        },
+                    ],
+                    "emphasis": {
+                        "itemStyle": {
+                            "shadowBlur": 10,
+                            "shadowOffsetX": 0,
+                            "shadowColor": "rgba(0, 0, 0, 0.5)",
+                        }
+                    },
+                }
+            ],
+        }
+        st_echarts(
+            options=options,
+            height="300px",
+        )
+
+
+# col1.button("PREDICT", on_click=predict_det, kwargs=det_input)
+
+
+# elif menu_id == 'Prognosis':
+#     _, col1, col2, _ = st.columns(4)
+#     col1.title('SARS-CoV-2 detection')
+#     col1.text('Press predict after filling in the form below.')
+#     col2.markdown("#")
+#     col2.markdown("#")
+#     col2.write("##")
+#     col2.write("##")
+
+#     for col in prog_cols1:
+#         prog_input[col] = col1.number_input(col)
+#         col2.text("")
+
+#     for col in cat_cols:
+#         prog_input[col] = col1.selectbox(col, ('F', 'M'))
+#         col2.text("")
+
+#     col2.write("##")
+#     col2.write("##")
+
+#     col1.button("PREDICT", on_click=predict_prog, kwargs=prog_input)

requirements.txt

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+pandas #==1.1.5
+numpy
+matplotlib
+seaborn
+scikit-learn
+xgboost
+catboost
+hyperopt
+torch #==1.7.1+cu101
+torchvision #==0.8.2+cu101
+# pytorch-lightning #==1.3.6
+pytorch-tabnet #==3.0.0
+pytorch_tabular #==0.7.0
+imblearn
+streamlit
+streamlit-lottie
+hydralit_components
+streamlit-modal
+streamlit-echarts
+# torchmetrics #==0.5.0
+# tab-transformer-pytorch
+# pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html