Create API_app

API_app

@@ -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 = '127.0.0.1', port = 7860)