Added files

Dockerfile

@@ -17,6 +17,18 @@
 
 # CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
 
+# FROM python:3.9
+
+# WORKDIR /code
+
+# COPY ./requirements.txt /code/requirements.txt
+
+# RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# COPY . .
+
+# CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
+
 FROM python:3.9
 
 WORKDIR /code
@@ -25,7 +37,15 @@ COPY ./requirements.txt /code/requirements.txt
 
 RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
-COPY . .
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
+COPY --chown=user . $HOME/app
 
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

@@ -1,7 +1,57 @@
 from fastapi import FastAPI
+import pickle
+import uvicorn
+import pandas as pd
 
 app = FastAPI()
 
-@app.get("/")
-def read_root():
-    return {"Hello": "World!"}
+# @app.get("/")
+# def read_root():
+#     return {"Hello": "World!"}
+
+
+# Function to load pickle file
+def load_pickle(filename):
+    with open(filename, 'rb') as file:
+        data = pickle.load(file)
+        return data
+    
+# Load pickle file
+ml_components = load_pickle('ml_sepsis.pkl') 
+
+# Components in the pickle file
+ml_model = ml_components['model']
+pipeline_processing = ml_components['pipeline']    
+
+@app.get('/Predict_Sepsis')
+async def predict(Plasma_glucose: int, Blood_Work_Result_1: int,
+                  Blood_Pressure: int, Blood_Work_Result_2: int,
+                    Blood_Work_Result_3: int, Body_mass_index: float, 
+                    Blood_Work_Result_4: float,Age: int, Insurance:float):
+    
+    data = pd.DataFrame({'Plasma glucose': [Plasma_glucose], 'Blood Work Result-1':	[Blood_Work_Result_1],
+                         'Blood Pressure': [Blood_Pressure], 'Blood Work Result-2': [Blood_Work_Result_2],
+                        'Blood Work Result-3': [Blood_Work_Result_3], 'Body mass index': [Body_mass_index],
+                        'Blood Work Result-4':	[Blood_Work_Result_4], 'Age': [Age], 'Insurance':[Insurance]})
+    
+    data_prepared = pipeline_processing.transform(data)
+
+    model_output = ml_model.predict(data_prepared).tolist()
+
+    prediction = make_prediction(model_output)
+
+    return prediction
+    
+    
+
+
+def make_prediction(data_prepared):
+
+    output_pred = data_prepared
+
+    if output_pred == 0:
+        output_pred = "Sepsis status is Negative"
+    else:
+        output_pred = "Sepsis status is Positive"
+        
+    return output_pred

requirements.txt

@@ -1,2 +1,4 @@
 fastapi[all]
-uvicorn[standard]
+uvicorn[standard]
+scikit-learn==1.2.2
+pandas