Duplicate from bright1/Sepsis-Prediction-API

Co-authored-by: Bright Eshun <bright1@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
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.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
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.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
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+# 
+FROM python:3.9
+
+# 
+WORKDIR /code
+
+# 
+COPY ./requirements.txt /code/requirements.txt
+
+# 
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# 
+COPY ./src /code/src
+
+# 
+CMD ["uvicorn", "src.app.app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: Sepsis Prediction API
+colorFrom: red
+colorTo: purple
+sdk: docker
+pinned: false
+duplicated_from: bright1/Sepsis-Prediction-API
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

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+tabulate
+fastapi[all]==0.95.2
+uvicorn[standard]==0.22.0
+numpy==1.20.1
+pandas==1.2.4
+scikit-learn==0.24.1
+jinja2==3.1.2
+

src/app/app.py

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+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+import uvicorn
+from fastapi import FastAPI, Request, File, UploadFile
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from src.utils import load_pickle, make_prediction, process_label, process_json_csv, output_batch, return_columns
+from src.module import Inputs
+import pandas as pd
+import numpy as np
+from typing import List
+
+
+# Create an instance of FastAPI
+app = FastAPI(debug=True)
+
+# get absolute path
+DIRPATH = os.path.dirname(os.path.realpath(__file__))
+
+# set path for pickle files
+model_path = os.path.join(DIRPATH, '..', 'assets', 'ml_components', 'model-1.pkl')
+transformer_path = os.path.join(DIRPATH, '..', 'assets', 'ml_components', 'preprocessor.pkl')
+properties_path = os.path.join(DIRPATH, '..', 'assets', 'ml_components', 'other-components.pkl')
+
+
+# Load the trained model, pipeline, and other properties
+model = load_pickle(model_path)
+transformer = load_pickle(transformer_path)
+properties = load_pickle(properties_path)
+
+# Configure static and template files
+app.mount("/static", StaticFiles(directory="src/app/static"), name="static") # Mount static files
+templates = Jinja2Templates(directory="src/app/templates") # Mount templates for HTML
+
+# Root endpoint to serve index.html template
+@app.get("/", response_class=HTMLResponse)
+async def root(request: Request):
+    return templates.TemplateResponse("index.html", {'request': request})
+
+# Health check endpoint
+@app.get("/health")
+def check_health():
+    return {"status": "ok"}
+
+# Model information endpoint
+@app.post('/model-info')
+async def model_info():
+    model_name = model.__class__.__name__ # get model name 
+    model_params = model.get_params() # get model parameters
+    features = properties['train features'] # get training feature
+    model_information =  {'model info': {
+            'model name ': model_name,
+            'model parameters': model_params,
+            'train feature': features}
+            }
+    return model_information # return model information
+ 
+
+# Prediction endpoint
+@app.post('/predict')
+async def predict(plasma_glucose: float, blood_work_result_1: float, 
+                  blood_pressure: float, blood_work_result_2: float, 
+                  blood_work_result_3: float, body_mass_index: float, 
+                  blood_work_result_4: float, age: int, insurance: bool):
+    
+    # Create a dataframe from inputs 
+    data = pd.DataFrame([[plasma_glucose,blood_work_result_1,blood_pressure,
+                           blood_work_result_2,blood_work_result_3,body_mass_index, 
+                           blood_work_result_4, age,insurance]], columns=return_columns())
+
+    # data_copy = data.copy() # Create a copy of the dataframe
+    labels, prob = make_prediction(data, transformer, model) # Get the labels  
+    response = output_batch(data, labels) # output results
+    return response
+
+
+# Batch prediction endpoint
+@app.post('/predict-batch')
+async def predict_batch(inputs: Inputs):
+    # Create a dataframe from inputs
+    data = pd.DataFrame(inputs.return_dict_inputs())
+    data_copy = data.copy() # Create a copy of the data
+    labels, probs = make_prediction(data, transformer, model) # Get the labels
+    response = output_batch(data, labels) # output results
+    return response
+
+
+
+# Upload data endpoint
+@app.post("/upload-data")
+async def upload_data(file: UploadFile = File(...)):
+    file_type = file.content_type # get the type of the uploaded file
+    valid_formats = ['text/csv', 'application/json'] # create a list of valid formats API can receive
+    if file_type not in valid_formats:
+        return JSONResponse(content={"error": f"Invalid file format. Must be one of: {', '.join(valid_formats)}"}) # return an error if file type is not included in the valid formats
+    
+    else:
+        contents = await file.read() # read contents in file
+        data= process_json_csv(contents=contents,file_type=file_type, valid_formats=valid_formats) # process files  
+        labels, probs = make_prediction(data, transformer, model) # Get the labels
+        response = output_batch(data, labels) # output results
+
+    return response
+
+
+# Run the FastAPI application
+if __name__ == '__main__':
+    uvicorn.run('app:app', reload=True)

src/app/static/styles.css

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+h1 {
+    color:rgb(81, 146, 43);
+}
+
+p {
+    font-weight: 600;
+}

src/app/templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <link rel="stylesheet" href="{{ url_for('static', path='/styles.css') }}">
+    <title>Document</title>
+</head>
+<body>
+  <h1>Welcome to the Sepsis API</h1>
+  <p>Kindly access the API Documentation link <a href="https://bright1-sepsis-prediction-api.hf.space/docs">here.</a></p>
+</body>
+</html>

src/assets/ml_components/model-1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3a27997d87bb2dec63f3dde72105ea2232c39c1c961ba92b2f36095db4078229
+size 937

src/assets/ml_components/other-components.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9b72e0521e300a6b0be14d89772ac467da5eabf078c21e85feb1dcc7a0a4701b
+size 471

src/assets/ml_components/preprocessor.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a9e55b9060711ca80ea27bcff559d20e52e9952a65388b53db41f696a771eba1
+size 2456

src/assets/ml_components/properties.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0efeb8aa27c6d6bae723817e03f53782a5fc0847440e900539a58977204de0ac
+size 387

src/module.py

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+from pydantic import BaseModel
+from typing import List
+
+
+class Input(BaseModel):
+    plasma_glucose: float
+    blood_work_result_1: float
+    blood_pressure: float
+    blood_work_result_2: float
+    blood_work_result_3: float
+    body_mass_index: float
+    blood_work_result_4: float
+    age: int 
+    insurance: bool
+
+
+class Inputs(BaseModel):
+    all: List[Input]
+
+    def return_dict_inputs(
+            cls,
+    ):
+        return [ input.dict() for input in cls.all]

src/utils.py

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+import pandas as pd
+import numpy as np
+import pickle
+from io import StringIO
+from functools import lru_cache
+
+@lru_cache(maxsize=100, )
+def load_pickle(filename):
+    with open(filename, 'rb') as file: # read file
+        contents = pickle.load(file) # load contents of file
+    return contents
+
+
+
+def feature_engineering(data):
+    data['Insurance'] = data['Insurance'].astype(int).astype(str) # run function to create new features
+    # create features 
+    data['All-Product']  = data['Blood Work Result-4'] * data['Blood Work Result-1']* data['Blood Work Result-2']* data['Blood Work Result-3'] * data['Plasma Glucose']* data['Blood Pressure'] * data['Age']* data['Body Mass Index'] # Multiply all numerical features
+
+    all_labels =['{0}-{1}'.format(i, i+500000000000) for i in range(0, round(2714705253292.0312),500000000000)]
+    data['All-Product_range'] = pd.cut(data['All-Product'], bins=(range(0, 3500000000000, 500000000000)), right=False, labels=all_labels)
+    
+    age_labels =['{0}-{1}'.format(i, i+20) for i in range(0, 83,20)]
+    data['Age Group'] = pd.cut(data['Age'], bins=(range(0, 120, 20)), right=False, labels=age_labels) # create categorical features for age
+
+    labels =['{0}-{1}'.format(i, i+30) for i in range(0, round(67.1),30)]
+    data['BMI_range'] = pd.cut(data['Body Mass Index'], bins=(range(0, 120, 30)), right=False, labels=labels) # create categorical features for bodey mass index
+
+    bp_labels =['{0}-{1}'.format(i, i+50) for i in range(0, round(122),50)] 
+    data['BP_range'] = pd.cut(data['Blood Pressure'], bins=(range(0, 200, 50)), right=False, labels=bp_labels) # create categorical features for blood pressure
+
+    labels =['{0}-{1}'.format(i, i+7) for i in range(0, round(17),7)]
+    data['PG_range'] = pd.cut(data['Plasma Glucose'], bins=(range(0, 28, 7)), right=False, labels=labels) # create categorical features for plasma glucose
+
+    data.drop(columns=['Blood Pressure', 'Age', 'Body Mass Index','Plasma Glucose', 'All-Product', 'Blood Work Result-3', 'Blood Work Result-2'], inplace=True) # drop unused columns
+
+    
+
+
+def combine_cats_nums(transformed_data, full_pipeline):
+    cat_features = full_pipeline.named_transformers_['categorical']['cat_encoder'].get_feature_names() # get the feature from the categorical transformer
+    num_features = ['Blood Work Result-1', 'Blood Work Result-4']
+    columns_ = np.concatenate([num_features, cat_features]) # concatenate numerical and categorical features
+    prepared_data = pd.DataFrame(transformed_data, columns=columns_) # create a dataframe from the transformed data
+    prepared_data = prepared_data.rename(columns={'x0_0':'Insurance_0', 'x0_1': 'Insurance_1'}) # rename columns
+    
+
+def make_prediction(data, transformer, model):
+    new_columns = return_columns() 
+    dict_new_old_cols = dict(zip(data.columns, new_columns)) # create a dict of original columns and new columns
+    data = data.rename(columns=dict_new_old_cols)
+    feature_engineering(data) # create new features
+    transformed_data = transformer.transform(data) # transform the data using the transformer    
+    combine_cats_nums(transformed_data, transformer)# create a dataframe from the transformed data 
+    # make prediction
+    label = model.predict(transformed_data) # make a prediction
+    probs = model.predict_proba(transformed_data) # predit sepsis status for inputs
+    return label, probs.max()
+
+
+
+# function to create a new column 'Bmi'
+def process_label(row):
+    if row['Predicted Label'] == 1:
+        return 'Sepsis status is Positive'
+    elif row['Predicted Label'] == 0:
+        return 'Sepsis status is Negative'
+    
+
+def return_columns():
+    # create new columns
+    new_columns =  ['Plasma Glucose','Blood Work Result-1', 'Blood Pressure', 
+                    'Blood Work Result-2', 'Blood Work Result-3', 'Body Mass Index',
+                    'Blood Work Result-4', 'Age', 'Insurance']
+    return new_columns
+
+
+def process_json_csv(contents, file_type, valid_formats):
+
+    # Read the file contents as a byte string
+    contents = contents.decode()  # Decode the byte string to a regular string
+    new_columns = return_columns() # return new_columns
+    # Process the uploaded file
+    if file_type == valid_formats[0]:
+        data = pd.read_csv(StringIO(contents)) # read csv files
+    elif file_type == valid_formats[1]:
+        data = pd.read_json(contents) # read json file
+    data = data.drop(columns=['ID']) # drop ID column
+    dict_new_old_cols = dict(zip(data.columns, new_columns)) # get dict of new and old cols
+    data = data.rename(columns=dict_new_old_cols) # rename colums to appropriate columns
+    return data
+
+        
+def output_batch(data1, labels):
+    data_labels = pd.DataFrame(labels, columns=['Predicted Label']) # convert label into a dataframe
+    data_labels['Predicted Label'] = data_labels.apply(process_label, axis=1) # change label to understanding strings
+    results_list = [] # create an empty lits
+    x = data1.to_dict('index') # convert  datafram into dictionary
+    y = data_labels.to_dict('index') # convert  datafram into dictionary
+    for i in range(len(y)):
+        results_list.append({i:{'inputs': x[i], 'output':y[i]}}) # append input and labels
+
+    final_dict = {'results': results_list}
+    return final_dict