Delete app.py

app.py

@@ -1,79 +0,0 @@
-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("Disease_DAndRFTrees.pkl", 'rb'))
-
-# Setup SHAP
-explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
-
-# Create the main function for server
-def main_func(HighBP, HighChol, BMI, Smoker, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, GenHlth, PhysHlth, Sex, Age):
-    new_row = pd.DataFrame.from_dict({'BP':highBP,'Chol':HighChol,
-              'BMI':BMI,'Smoker':Smoker,'PhysActivity':PhysActivity,
-              'Fruits':Fruits, 'Veggies':Veggies,'HvyAlcoholConsump': HvyAlcoholConsump,'GenHlth':GenHlth,
-                                     'PhysHlth':PhysHlth,'Sex':Sex,'Age':Age}, 
-                                     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 {"Low Chance": float(prob[0][0]), "High Chance": 1-float(prob[0][0])}, local_plot
-
-# Create the UI
-title = "**Diabetes Predictor & Interpreter** 🪐"
-description1 = """This app takes info from subjects and predicts their diabetes 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. 🤞
-""" 
-
-with gr.Blocks(title=title) as demo:
-    gr.Markdown(f"## {title}")
-    gr.Markdown(description1)
-    gr.Markdown("""---""")
-    gr.Markdown(description2)
-    gr.Markdown("""---""")
-    
-    Age = gr.Number(label="age Score", value=40)
-    BMI = gr.Number(label="BMI Score", value=98)
-    Sex = gr.Slider(label="sex Score", minimum=0, maximum=1, value=1, step=1)
-    Smoker = gr.Slider(label="Smoker Score", minimum=0, maximum=1, value=1, step=1)
-    PhysActivity = gr.Slider(label="Physical Activity Score", minimum=0, maximum=1, value=1, step=1)
-    Fruits = gr.Slider(label="Fruits Score", minimum=0, maximum=1, value=1, step=1)
-    Veggies = gr.Slider(label="Veggies Score", minimum=0, maximum=1, value=1, step=1)
-    HvyAlcoholConsump = gr.Slider(label="Alcohol Consumption Score", minimum=0, maximum=1, value=1, step=1)
-    HighBP = gr.Slider(label="BP Score", minimum=1, maximum=1, value=1, step=1)
-    HighChol = gr.Slider(label="Cholesterol Score", minimum=1, maximum=1, value=1, step=1)
-    GenHlth = gr.Slider(label="GenHlth Score", minimum=1, maximum=5, value=4, step=1)
-    PhysHealth = gr.Number(label="PhysHealth Score", 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:')
-
-    submit_btn.click(
-        main_func,
-        [HighBP, HighChol, BMI, Smoker, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, GenHlth, PhysHlth, Sex, Age],
-        [label,local_plot], api_name="Diabetes_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]], [HighBP, HighChol, BMI, Smoker, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, GenHlth, PhysHlth, Sex, Age], [label,local_plot], main_func, cache_examples=True)
-
-demo.launch()