Upload 14 files

.Dockerignore

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+venv/
+__pycache__/

Assets/Final_01_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:02d2c340bf2e8a1977327fb4d92cf337d3106ee0a93a66ea50a87ce7db9fefbf
+size 107062

Assets/Final_02_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2d931a4eed525b88e7e39f617091e75fed694149f523e1183d03e6755ec2b045
+size 280960

Assets/Final_03_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:95146dc19fdd476db63b0129be3302f6ff270e9763b327f2179c306365c24c3d
+size 33436

Assets/label_encoder.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:36dfa139c49c8671caadcbb80691bbcaf63c4e38b98edc137eb2cfabcf7eb8dc
+size 548

Assets/numerical_imputer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6e9b4073656b3acdc3ab91cea2949192549ec7c7b4d239754eca8db853ca0d6a
+size 943

Assets/scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4c394755caeda935f17dcd6f78e49f5f5cac6639c1980b3c6479e3ec816f4b88
+size 815

Data/Paitients_Files_Test.csv

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+ID,PRG,PL,PR,SK,TS,M11,BD2,Age,Insurance

Data/Paitients_Files_Train.csv

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+ID,PRG,PL,PR,SK,TS,M11,BD2,Age,Insurance,Sepssis

Dockerfile

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+# Use the official Python base image
+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /code
+
+# Copy the requirements file to the working directory
+COPY requirements.txt /code/requirements.txt
+
+# Install the Python dependencies
+RUN pip install -r requirements.txt
+
+# Copy the application code to the working directory
+COPY ./src /code/src
+
+# Expose the port on which the application will run
+EXPOSE 8000
+
+# Run the FastAPI application using uvicorn server
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

main.py

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+from fastapi import FastAPI, HTTPException
+import uvicorn
+import os
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.impute import SimpleImputer
+import joblib
+
+app = FastAPI(debug=True)
+
+def load_model():
+    cwd = os.getcwd()
+    destination = os.path.join(cwd, "Assets")
+
+    imputer_filepath = os.path.join(destination, "numerical_imputer.joblib")
+    scaler_filepath = os.path.join(destination, "scaler.joblib")
+    model_filepath = os.path.join(destination, "Final_01_model.joblib")
+
+    num_imputer = joblib.load(imputer_filepath)
+    scaler = joblib.load(scaler_filepath)
+    model = joblib.load(model_filepath)
+
+    return num_imputer, scaler, model
+
+numerical_imputer, scaler, model = load_model()
+
+@app.get("/")
+async def read_root():
+    return {"message": "Welcome To The Sepsis Prediction API"}
+
+@app.post("/predict_sepsis")
+async def predict_sepsis(PRG: float, PL: float, PR: float, SK: float, TS: float, M11: float, BD2: float, Age: float, Insurance: int):
+    sepsis_data = {
+        'PRG': PRG,
+        'PL': PL,
+        'PR': PR,
+        'SK': SK,
+        'TS': TS,
+        'M11': M11,
+        'BD2': BD2,
+        'Age': Age,
+        'Insurance': Insurance
+    }
+
+    input_data = pd.DataFrame([sepsis_data])  # Create a DataFrame from the dictionary
+
+    input_imputed = numerical_imputer.transform(input_data)
+    input_scaled = scaler.transform(input_imputed)
+
+    prediction = model.predict(input_scaled)
+
+    sepsis_status = "Positive" if prediction == 1 else "Negative"
+
+    probabilities = model.predict_proba(input_scaled)[0]
+    probability = probabilities[1] if prediction == 1 else probabilities[0]
+
+    if prediction == 1:
+        status_icon = "✔"
+        sepsis_explanation = "Sepsis is a life-threatening condition caused by an infection. A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention."
+    else:
+        status_icon = "✘"
+        sepsis_explanation = "Sepsis is a life-threatening condition caused by an infection. A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
+
+    statement = f"The patient's sepsis status is {sepsis_status} {status_icon} with a probability of {probability:.2f}. {sepsis_explanation}"
+
+    user_input_statement = f"Please note this is the user-inputted data: {sepsis_data}"
+
+    result = {
+        'predicted_sepsis': sepsis_status,
+        'statement': statement,
+        'user_input_statement': user_input_statement,
+        'probability': probability
+    }
+
+    return result
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)

requirements.txt

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+fastapi==0.100.0
+gradio==3.36.1
+huggingface-hub==0.16.4
+joblib==1.3.1
+matplotlib==3.7.2
+matplotlib-inline==0.1.6
+numpy==1.25.0
+plotly==5.16.1
+pydantic==2.0.2
+pydantic_core==2.1.2
+scikit-learn==1.3.0
+scipy==1.11.1
+seaborn==0.12.2
+streamlit==1.24.1
+threadpoolctl==3.1.0
+uvicorn==0.22.0