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README.md

@@ -1,10 +1,10 @@
 ---
-title: Heart Attack Predictor Class Project
-emoji: 🐨
-colorFrom: gray
-colorTo: pink
+title: Group Eight
+emoji: 🚀
+colorFrom: indigo
+colorTo: green
 sdk: gradio
-sdk_version: 3.39.0
+sdk_version: 3.27.0
 app_file: app.py
 pinned: false
 license: mit

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
+
+
+theme = gr.themes.Default(primary_hue="blue").set(
+    background_fill_primary="#D3D3D3",
+    block_background_fill="#D3D3D3",
+)
+
+
+# load the model from disk
+loaded_model = pickle.load(open("heart_xgbV2.pkl", 'rb'))
+
+# Setup SHAP
+explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
+
+gender_dict = {"Male":0,"Female":1}
+cp_dict = {"Typical Angina":0, "Atypical Angina":1, "Non-Anginal":2, "Asymptomatic":3}
+fbs_dict = {"Yes":1,"No":0}
+exng_dict = {"Yes":1,"No":0}
+restecg_dict = {"Normal":0, "Having ST-T abnormality":1, "Showing probable or definite left ventricular hypertrophy by Estes' Criteria":2}
+thall_dict = {"Fixed Defect":1, "Normal Blood Flow":2, "Reversible Defect":3}
+slp_dict = {"Upsloping":1, "Flat":2, "Downsloping":3}
+
+# Create the main function for server
+def main_func(age, sex, cp, trtbps, chol, fbs, restecg,thalachh,exng,oldpeak,slp,caa,thall):
+    new_row = pd.DataFrame.from_dict({'age':age,'sex':gender_dict[sex],
+              'cp':cp_dict[cp],'trtbps':trtbps,'chol':chol,
+              'fbs':fbs_dict[fbs], 'restecg':restecg_dict[restecg], 'thalachh':thalachh, 'exng':exng_dict[exng],
+                                     'oldpeak':oldpeak,'slp':slp_dict[slp],'caa':caa,'thall':thall_dict[thall]}, 
+                                     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 {"Lower Chance of a Heart Attack": float(prob[0][0]), "Higher Chance of a Heart Attack": 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."
+
+description_notmedical="**Do not use for medical diagnosis.**"
+
+
+description2 = "**Fill all the options** or no result will be generated!!!**"
+
+
+description3 = "To use the app, please fill all the options, and click on Analyze. 🤞"
+
+
+descriptionExamples = "If you would like to see how the model works, please scroll down and try one of the examples!"
+
+
+##Pinak
+with gr.Blocks(title=title, theme=theme) as demo:
+
+    gr.Markdown("<span style='color: #FF0000;font-size: 20px'> **Heart Attack Predictor & Interpreter** 🪐</span>")
+    gr.Markdown("""---""")
+    gr.Markdown("<span style='font-size: 20px;'> **Do not use for medical diagnosis.**")
+    gr.Markdown("""---""")
+    gr.Markdown("<span style='font-size: 16px;'> If you would like to see how the model works, please scroll down and try one of the examples!")
+    gr.Markdown("""---""")
+    gr.Markdown("<span style='font-size: 16px;'> This app takes info from subjects and predicts their heart attack likelihood.")
+    gr.Markdown("""---""")
+    gr.Markdown("<span style='font-size: 16px;'> To use the app, please fill in all the options, and click on Analyze. 🤞")
+    gr.Markdown("<span style='font-size: 16px;'> **Fill all the options or no result will be generated!!!**")
+    gr.Markdown("""---""")
+    
+    with gr.Row():
+        with gr.Column():
+            age = gr.Number(label="What is your age?", value=40)
+        with gr.Column():
+            slp = gr.Dropdown(["Upsloping", "Flat", "Downsloping"], label="What was the slope of the peak exercise ST segment?")
+            
+    with gr.Row(): 
+        with gr.Column():
+            sex = gr.Radio(["Female", "Male"], label = "What is your sex?")
+            cp = gr.Radio(["Typical Angina", "Atypical Angina", "Non-Anginal", "Asymptomatic"], label = "What kind of chest pain is it?")
+        with gr.Column():
+            restecg = gr.Radio(["Normal", "Having ST-T abnormality", "Showing probable or definite left ventricular hypertrophy by Estes' Criteria"], 
+                                  label = "What is your resting ECG result?")
+            
+    with gr.Row():
+        with gr.Column():
+            fbs = gr.Radio(["Yes", "No"], label = "Is your fasting Blood Sugar >120 mg/dl?")
+        with gr.Column():
+            exng = gr.Radio(["Yes", "No"], label = "Do you have Exercise Induced Angina?")
+    with gr.Row():
+        with gr.Column():
+            caa = gr.Radio([1, 2, 3], label="How many vessels were colored by the fluoroscopy?")
+            
+        with gr.Column():
+            thall = gr.Radio(["Fixed Defect", "Normal Blood Flow", "Reversible Defect"],  label="What is your Thalassemia condition?")
+            
+    with gr.Row():
+        with gr.Column():
+            trtbps = gr.Slider(label = "What is your resting blood Pressure (in mm Hg)?", minimum = 10, maximum = 250, value = 100, step = 1)
+            
+        with gr.Column():
+            chol = gr.Slider(label = "What is your cholesterol in mg/dl (via BMI sensor)?", minimum = 30, maximum = 300, value = 180, step = 1)
+    with gr.Row():      
+        with gr.Column():
+            oldpeak = gr.Slider(label = "What was the ST depression induced by exercise relative to rest?", minimum = 0, maximum = 6.2, step = 0.1)
+        with gr.Column():
+            thalachh = gr.Slider(label="What is your maximum heart rate?", minimum = 60, maximum = 250, value=100, step = 1)
+            
+
+    with gr.Row():
+            submit_btn = gr.Button("Analyze")
+
+##Do not need to touch    
+    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,
+            [age, sex, cp, trtbps, chol, fbs, restecg,thalachh,exng,oldpeak,slp,caa,thall],
+            [label,local_plot], api_name="Heart_Predictor"
+        )
+
+    gr.Examples([[24, "Male", "Typical Angina", 130, 150, "Yes", "Having ST-T abnormality",170, "Yes", 5.1, "Flat", 2, "Normal Blood Flow"],
+                 [59, "Female", "Non-Anginal", 150, 170, "No", "Showing probable or definite left ventricular hypertrophy by Estes' Criteria",190, "No", 6, "Upsloping", 3, "Reversible Defect"]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall], [label,local_plot], main_func, cache_examples=True)
+
+demo.launch() 

gitattributes

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+*.pickle filter=lfs diff=lfs merge=lfs -text
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heart_xgb.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:15c0afec50d47e4a5842340d8c47b8a14be0e60f6d2bd4898974a210fd795903
+size 138725

heart_xgbV2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c89450258148337aaf93e65ca5526dcabe996126869ad0c6f4f2a7d37ffb28f8
+size 132944

requirements.txt

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+gradio==3.1.3
+Pillow
+yake
+pandas
+sklearn
+shap
+xgboost
+matplotlib
+numpy
+streamlit