Upload 4 files

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+P6_toolkit filter=lfs diff=lfs merge=lfs -text

API_app.py

@@ -0,0 +1,96 @@
+"""
+FastAPI script for Sepssis and model prediction
+Author: Equity
+Date: May.30th 2023
+"""
+
+
+# The library for the API Code
+from fastapi import FastAPI
+import pickle
+import uvicorn
+from pydantic import BaseModel
+import pandas as pd
+
+
+
+# Declare the data with its components and their type
+class model_input(BaseModel):
+    
+    PRG: int
+    PL: int
+    PR: int
+    SK: int
+    TS: int
+    M11: float
+    BD2: float
+    Age: int
+    Insurance:int
+
+
+app = FastAPI(title = 'Sepssis API',
+              description = 'An API that takes input and display the predictions',
+              version = '0.1.0')
+
+# Load the saved data
+toolkit = "P6_toolkit"
+
+def load_toolkit(filepath = toolkit):
+    with open(toolkit, "rb") as file:
+        loaded_toolkit = pickle.load(file)
+    return loaded_toolkit
+
+toolkit = load_toolkit()
+scaler = toolkit["scaler"]
+model = toolkit["model"]
+
+
+@app.get("/")
+async def hello():
+    return "Welcome to our model API"
+
+
+
+@app.post("/Sepssis")
+async def prediction(input:model_input):
+   data = {
+           'PRG': input.PRG, 
+           'PL': input.PL,
+           'PR': input.PR,
+           'SK': input.SK,
+           'TS': input.TS,
+           'M11': input.M11,
+           'BD2': input.BD2,
+           'Age': input.Age,
+           'Insurance': input.Insurance,
+                }
+   
+# prepare the data as a dataframe
+   df = pd.DataFrame(data, index=[0])
+
+
+   #numerical features
+   numeric_columns =  [ 'PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age','Insurance']
+   
+   #scaling
+   Scaler = scaler.transform(df[numeric_columns])
+   Scaled = pd.DataFrame(Scaler)                  
+   prediction = model.predict(Scaled).tolist()
+   probability = model.predict_proba(Scaled)
+
+
+    # Labelling Model output
+   if (prediction[0] < 0.5):
+         prediction = "Negative. This person has no Sepssis"
+   else: 
+      prediction = "Positive. This person has Sepssis"
+   data['prediction'] = prediction  
+   return data
+   
+   
+        
+
+
+# Launch the app
+if __name__ == "__main__":
+ uvicorn.run("API_app:app",host = '0.0.0.0', port = 7860)

Dockerfile (1).txt

@@ -0,0 +1,28 @@
+FROM python:3.9
+
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+
+
+#COPY . . 
+CMD ["python","API_app.py"]

P6_toolkit

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67301fa0e7ceab779e3047c8d399db2c83075496b832c5bdd93ecb5399bd5657
+size 1178776

requirements (1).txt

@@ -0,0 +1,4 @@
+pytest
+scikit-learn
+fastapi[all]
+pandas