Upload app.py

app.py

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+import pickle
+import pandas as pd
+import shap
+from shap.plots._force_matplotlib import draw_additive_plot
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
+# load the model from disk
+loaded_model = pickle.load(open("glioma_xgb.pkl", 'rb'))
+
+# Setup SHAP
+explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
+
+# Create the main function for server
+def main_func(Gender, Age_at_diagnosis, IDH1, TP53, ATRX, PTEN, EGFR, CIC, MUC16, PIK3CA, NF1, PIK3R1, FUBP1, RB1, NOTCH1, BCOR, CSMD3, SMARCA4, GRIN2A, IDH2, FAT4, PDGFRA):
+    new_row = pd.DataFrame.from_dict({'Gender':Gender,
+              'Age_at_diagnosis':Age_at_diagnosis,'IDH1':IDH1,'TP53':TP53,
+              'ATRX':ATRX, 'PTEN':PTEN,'EGFR':EGFR,'CIC':CIC,
+              'MUC16':MUC16,'PIK3CA':PIK3CA,'NF1':NF1,'PIK3R1':PIK3R1, 'FUBP1': FUBP1, 'RB1': RB1, 'NOTCH1': NOTCH1,
+              'BCOR': BCOR, 'CSMD3': CSMD3, 'SMARCA4': SMARCA4, 'GRIN2A': GRIN2A, 'IDH2': IDH2, 'FAT4': FAT4, 'PDGFRA': PDGFRA}, 
+               orient = 'index').transpose()
+    
+    prob = loaded_model.predict_proba(new_row)
+    
+    shap_values = explainer(new_row)
+    # plot = shap.force_plot(shap_values[0], matplotlib=True, figsize=(30,30), show=False)
+    # plot = shap.plots.waterfall(shap_values[0], max_display=6, show=False)
+    plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
+
+    plt.tight_layout()
+    local_plot = plt.gcf()
+    plt.close()
+    
+    return {"Chance of Having GBM Tumor": 1-float(prob[0][0]), "Chance of Having LGG Tumor": float(prob[0][0])}, local_plot
+
+# Create the UI
+title = "**Glioma Predictor & Interpreter** 🪐"
+description1 = """This app takes info from subjects and predicts the severity of their brain tumor (LGG or GBM). Do not use for medical diagnosis."""
+
+description2 = """
+To use the app, click on one of the examples, or adjust the values of the factors, and click on Analyze. 🤞
+""" 
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description1)
+    gr.Markdown("""---""")
+    gr.Markdown(description2)
+    gr.Markdown("""---""")
+
+    with gr.Row():
+        Gender = gr.Radio(["Female", "Male"], label="Gender", type = "index")
+        Age_at_diagnosis = gr.Number(label="Age at Diagnosis")
+    with gr.Row():
+        IDH1 = gr.Radio(["No", "Yes"], label="IDH1 Mutation", type="index") 
+        TP53 = gr.Radio(["No", "Yes"], label="TP53 Mutation", type="index")
+        ATRX = gr.Radio(["No", "Yes"], label="ATRX Mutation", type="index")
+    with gr.Row():
+        PTEN = gr.Radio(["No", "Yes"], label="PTEN Mutation", type="index")
+        EGFR = gr.Radio(["No", "Yes"], label="EGFR Mutation", type="index")
+        CIC = gr.Radio(["No", "Yes"], label="CIC Mutation", type="index")
+    with gr.Row():
+        MUC16 = gr.Radio(["No", "Yes"], label="MUC16 Mutation", type="index")
+        PIK3CA = gr.Radio(["No", "Yes"], label="PIK3CA Mutation", type="index")
+        NF1 = gr.Radio(["No", "Yes"], label="NF1 Mutation", type="index")
+    with gr.Row():
+        PIK3R1 = gr.Radio(["No", "Yes"], label="PIK3R1 Mutation", type="index")
+        FUBP1 = gr.Radio(["No", "Yes"], label="FUBP1 Mutation", type="index")
+        RB1 = gr.Radio(["No", "Yes"], label="RB1 Mutation", type="index")
+    with gr.Row():
+        NOTCH1 = gr.Radio(["No", "Yes"], label="NOTCH1 Mutation", type="index")
+        BCOR = gr.Radio(["No", "Yes"], label="BCOR Mutation", type="index")
+        CSMD3 = gr.Radio(["No", "Yes"], label="CSMD3 Mutation", type="index")
+    with gr.Row():
+        SMARCA4 = gr.Radio(["No", "Yes"], label="SMAECA4 Mutation", type="index")
+        GRIN2A = gr.Radio(["No", "Yes"], label="GRIN2A Mutation", type="index")
+        IDH2 = gr.Radio(["No", "Yes"], label="IDH2 Mutation", type="index")
+        FAT4 = gr.Radio(["No", "Yes"], label="FAT4 Mutation", type="index")
+        PDGFRA = gr.Radio(["No", "Yes"], label="PDGFRA Mutation", type="index")
+
+
+
+
+
+
+    
+    submit_btn = gr.Button("Analyze")
+
+    with gr.Column(visible=True) as output_col:
+        label = gr.Label(label = "Predicted Label")
+        local_plot = gr.Plot(label = 'Grade:')
+
+    submit_btn.click(
+        main_func,
+        [Gender, Age_at_diagnosis, IDH1, TP53, ATRX, PTEN, EGFR, CIC, MUC16, PIK3CA, NF1, PIK3R1, FUBP1, RB1, NOTCH1, BCOR, CSMD3, SMARCA4, GRIN2A, IDH2, FAT4, PDGFRA],
+        [label,local_plot], api_name="Glioma_Predictor"
+    )
+    
+    gr.Markdown("### Click on any of the examples below to see how it works:")
+    gr.Examples([["Male",24,"Yes","No","Yes","Yes","Yes","No","Yes","Yes","Yes","Yes","Yes","No","No","No","No","Yes","No","Yes","No","Yes"], ["Male",70,"No","No","No","No","No","No","No","No","No","Yes","No","Yes","No","No","No","No","No","No","No", "No"]], [Gender, Age_at_diagnosis, IDH1, TP53, ATRX, PTEN, EGFR, CIC, MUC16, PIK3CA, NF1, PIK3R1, FUBP1, RB1, NOTCH1, BCOR, CSMD3, SMARCA4, GRIN2A, IDH2, FAT4, PDGFRA], [label,local_plot], main_func, cache_examples=True)
+
+demo.launch()