Update app.py

app.py

@@ -7,18 +7,16 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 # load the model from disk
-loaded_model = pickle.load(open("glioma_xgb.pkl", 'rb'))
+loaded_model = pickle.load(open("salar_xgb_team.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}, 
+def main_func(age, education-num, sex, capital-gain, capital-loss, hours-per-week, salary-class):
+    new_row = pd.DataFrame.from_dict({'age':age,
+              'education-num':education-num,'sex':sex,'capital-gain':capital-gain,
+              'capital-loss':capital-loss, 'hours-per-week':hours-per-week,'salary-class':salary-class}, 
                orient = 'index').transpose()
     
     prob = loaded_model.predict_proba(new_row)
@@ -32,72 +30,54 @@ def main_func(Gender, Age_at_diagnosis, IDH1, TP53, ATRX, PTEN, EGFR, CIC, MUC16
     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
+    return {
+        "Chance of Earning > $50K": float(prob[0][1]),
+        "Chance of Earning ≤ $50K": 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."""
+title = "**Household Income Predictor** 💰"
+description1 = """This app uses your input to predict whether a household earns more or less than $50K per year"""
 
-description2 = """
-To use the app, click on one of the examples, or adjust the values of the factors, and click on Analyze. 🤞
-""" 
+description2 = """Adjust the values below and click 'Analyze' to see the prediction and explanation."""
 
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown(description1)
-    gr.Markdown("""---""")
+    gr.Markdown("---")
     gr.Markdown(description2)
-    gr.Markdown("""---""")
+    gr.Markdown("---")
 
     with gr.Row():
-        Gender = gr.Radio(["Female", "Male"], label="Gender", type = "index")
-        Age_at_diagnosis = gr.Number(label="Age at Diagnosis")
+        age = gr.Number(label="Age", value=35)
+        education_num = gr.Number(label="Education Level (numeric)", value=10)
     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")
-
-
+        sex = gr.Radio(["Male", "Female"], label="Sex", type="index")  # Male = 0, Female = 1
+        capital_gain = gr.Number(label="Capital Gain", value=0)
+        capital_loss = gr.Number(label="Capital Loss", value=0)
+        hours_per_week = gr.Number(label="Hours per Week", value=40)
 
-
-
-
-    
     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:')
+        label = gr.Label(label="Predicted Probabilities")
+        local_plot = gr.Plot(label="Top SHAP Features")
 
     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"
+        [age, education_num, sex, capital_gain, capital_loss, hours_per_week],
+        [label, local_plot],
+        api_name="Salary_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)
+
+    gr.Markdown("### Examples:")
+    gr.Examples([
+        [28, 12, 0, 0, 0, 40],  # Male, younger, more educated
+        [50, 9, 1, 0, 0, 30]    # Female, mid-education, fewer hours
+    ], 
+    inputs=[age, education_num, sex, capital_gain, capital_loss, hours_per_week], 
+    outputs=[label, local_plot],
+    fn=main_func,
+    cache_examples=True)
 
 demo.launch()