Update app.py

app.py

@@ -12,71 +12,72 @@ 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
+# Main prediction function
 def main_func(age, education_num, sex, capital_gain, capital_loss, hours_per_week):
-    new_row = pd.DataFrame.from_dict({'age':age,'sex':sex,
-              'cp':cp,'trtbps':trtbps,'chol':chol,
-              'fbs':fbs, 'restecg':restecg,'thalachh':thalachh,'exng':exng,
-                                     'oldpeak':oldpeak,'slp':slp,'caa':caa,'thall':thall}, 
-                                     orient = 'index').transpose()
-    
+    sex_binary = 0 if sex == "Male" else 1
+    new_row = pd.DataFrame({
+        'age': [age],
+        'education-num': [education_num],
+        'sex': [sex_binary],
+        'capital-gain': [capital_gain],
+        'capital-loss': [capital_loss],
+        'hours-per-week': [hours_per_week]
+    })
+ 
     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)
-
+ 
+    # SHAP bar plot
+    plot = shap.plots.bar(shap_values[0], max_display=6, show=False)
     plt.tight_layout()
     local_plot = plt.gcf()
     plt.close()
-    
-    return {"Low Chance": float(prob[0][0]), "High Chance": 1-float(prob[0][0])}, local_plot
-
-# Create the UI
-title = "**Heart Attack Predictor & Interpreter** 🪐"
-description1 = """This app takes info from subjects and predicts their heart attack likelihood. 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. 🤞
-""" 
+ 
+    return {"≤50K": float(prob[0][0]), ">50K": float(prob[0][1])}, local_plot
 
+# Gradio UI
+title = "**Salary Predictor & SHAP Explainer** 💼"
+description1 = "This app uses demographic and financial info to predict if someone earns over 50K annually."
+description2 = "Adjust the inputs below and click Analyze to see the prediction and SHAP feature importance."
+ 
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown(description1)
-    gr.Markdown("""---""")
+    gr.Markdown("---")
     gr.Markdown(description2)
-    gr.Markdown("""---""")
-    
-    age = gr.Number(label="age Score", value=40)
-    sex = gr.Slider(label="sex Score", minimum=0, maximum=1, value=1, step=1)
-    cp = gr.Slider(label="cp Score", minimum=1, maximum=5, value=4, step=1)
-    trtbps = gr.Slider(label="trtbps Score", minimum=1, maximum=5, value=4, step=1) 
-    chol = gr.Slider(label="chol Score", minimum=1, maximum=5, value=4, step=1)
-    fbs = gr.Slider(label="fbs Score", minimum=1, maximum=5, value=4, step=1)
-
-    restecg = gr.Slider(label="restecg Score", minimum=1, maximum=5, value=4, step=1)
-    thalachh = gr.Slider(label="thalachh Score", minimum=1, maximum=5, value=4, step=1)
-   
-    exng = gr.Slider(label="exng Score", minimum=1, maximum=5, value=4, step=1)
-    oldpeak = gr.Slider(label="oldpeak Score", minimum=1, maximum=5, value=4, step=1)
-    slp = gr.Slider(label="slp Score", minimum=1, maximum=5, value=4, step=1)
-    caa = gr.Slider(label="caa Score", minimum=1, maximum=5, value=4, step=1)
-    thall = gr.Slider(label="thall Score", minimum=1, maximum=5, value=4, step=1)
-    
+    gr.Markdown("---")
+ 
+    age = gr.Slider(label="Age", minimum=18, maximum=70, value=35)
+    education_num = gr.Slider(label="Education Number", minimum=1, maximum=16, value=10)
+    sex = gr.Radio(["Male", "Female"], label="Sex")
+    capital_gain = gr.Number(label="Capital Gain", value=0)
+    capital_loss = gr.Number(label="Capital Loss", value=0)
+    hours_per_week = gr.Slider(label="Hours per Week", minimum=1, maximum=100, 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 = 'Shap:')
-
+        label = gr.Label(label="Predicted Probability")
+        local_plot = gr.Plot(label="SHAP Feature Importance")
+ 
     submit_btn.click(
         main_func,
-        [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall],
-        [label,local_plot], api_name="Heart_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([[24,0,4,4,5,5,4,4,5,5,1,2,3], [24,0,4,4,5,3,3,2,1,1,1,2,3]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall], [label,local_plot], main_func, cache_examples=True)
-
+ 
+    gr.Markdown("### Try one of the following examples:")
+    gr.Examples(
+        examples=[
+            [28, 12, "Male", 0, 0, 45],
+            [52, 14, "Female", 7688, 0, 60],
+            [35, 9, "Male", 0, 1902, 40]
+        ],
+        inputs=[age, education_num, sex, capital_gain, capital_loss, hours_per_week],
+        outputs=[label, local_plot],
+        fn=main_func,
+        cache_examples=True
+    )
+ 
 demo.launch()