Duplicate from Jaffermirza17/ProjectPythonClass

Co-authored-by: Jaffer Mirza <Jaffermirza17@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: ProjectPythonClass
+emoji: 📊
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 3.27.0
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: Jaffermirza17/ProjectPythonClass
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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("heart_xgb.pkl", 'rb'))
+
+# Setup SHAP
+explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
+
+# 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':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()
+    
+    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=8, 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 = "**Heart Attack Predictor & Interpreter** 🪐"
+description1 = """This app takes info from subjects and predicts their heart attack likelihood. Do not use these results for an actual medical diagnosis."""
+
+description2 = """
+To use the app, simply adjust the inputs and click the "Analyze" button. You can also click one of the examples below to see how it's done!
+""" 
+
+with gr.Blocks(title=title) as demo:
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown(f"# {title}")
+            gr.Markdown(f"## How does it work?")
+            gr.Markdown(description1)
+            gr.Markdown("""---""")
+            gr.Markdown(description2)
+
+    gr.Markdown("""---""")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown(f"## Edit the Inputs Below:")
+            gr.Markdown("""---""")
+
+            with gr.Row():
+                age = gr.Number(label="Age", info="How old are you?", value=40)
+                # sex = gr.Radio(["Male", "Female"], label = "What Gender are you?", type = "index")
+                sex = gr.Radio(["Male", "Female"], label="Sex", info="What gender are you?", type="index")
+                # sex = gr.Radio(choices=["Male", "Female"])
+            
+            cp = gr.Radio(["Typical Angina", "Atypical Angina", "Non-anginal Pain", "Asymptomatic"], label="Chest Pain", info="What kind of chest pain do you have?", type="index")
+            # cp = gr.Slider(label="Chest Pain Type", minimum=1, maximum=5, value=4, step=1)
+             # trtbps = gr.Slider(label="Resting blood pressure (in mm Hg)", minimum=1, maximum=200, value=4, step=1) 
+            trtbps = gr.Number(label="trtbps", value=100) 
+            chol = gr.Number(label="chol", value=70) 
+            fbs = gr.Radio(["False", "True"], label="fbs",  info="Is your fasting blood sugar > 120 mg/dl?" , type="index")
+
+            # restecg = gr.Slider(label="Resting ECG Score", minimum=1, maximum=5, value=4, step=1)
+            restecg = gr.Dropdown(["Normal", "Having ST-T wave abnormality", "Showing probable or definite left ventricular hypertrophy by Estes' criteria"], label="rest_ecg", type="index")
+            thalachh = gr.Slider(label="thalach Score", minimum=1, maximum=205, value=4, step=1)
+            exng = gr.Radio(["No", "Yes"], label="Exercise Induced Angina", type="index")
+            oldpeak = gr.Slider(label="Oldpeak Score", minimum=1, maximum=10, value=4, step=1)
+            slp = gr.Slider(label="Slp Score", minimum=1, maximum=5, value=4, step=1)
+            caa = gr.Slider(label="Number of Major Vessels", minimum=1, maximum=3, value=3, step=1)
+            thall = gr.Slider(label="Thall Score", minimum=1, maximum=5, value=4, step=1)
+
+            
+            
+            
+
+        with gr.Column():
+            gr.Markdown(f"## Output:")
+            gr.Markdown("""---""")
+            with gr.Column(visible=True) as output_col:
+                label = gr.Label(label = "Predicted Label")
+                local_plot = gr.Plot(label = 'Shap:')
+
+            gr.Markdown(f"## Examples:")
+            gr.Markdown("""---""")
+            gr.Markdown("### Click on any of the examples below to see how it works:")
+            gr.Examples([[24,"Male","Typical Angina",4,5,"True","Normal",4,"No",5,1,2,3], [24,"Female","Asymptomatic",4,5,"False","Normal",2,"Yes",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)
+
+        
+    submit_btn = gr.Button("Analyze", variant="primary")
+
+
+    gr.Markdown("""---""")
+    gr.Markdown(f"## Data Dictionary:")
+    gr.Markdown("""
+    
+Age : Age of the patient
+Sex : Sex of the patient
+trtbps : resting blood pressure (in mm Hg)
+chol : cholestoral in mg/dl fetched via BMI sensor
+fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
+rest_ecg : resting electrocardiographic results
+    Value 0: normal
+    Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
+    Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
+    
+thalach : maximum heart rate achieved
+target : 0 = less chance of heart attack 1= more chance of heart attack""")
+    
+
+    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"
+    )
+
+    
+demo.launch()

heart_xgb.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