first upload

Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.10-slim
+
+RUN apt update && apt install -y default-jdk
+
+WORKDIR /app
+COPY . .
+RUN python -m pip install --upgrade pip
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+RUN echo 'export PATH="/root/.local/bin:$PATH"' >> /root/.bashrc
+
+CMD ["uvicorn", "--host", "0.0.0.0", "--port", "7860", "--app-dir=api", "--reload", "--reload-dir", "api", "main:app"]

api/main.py

@@ -0,0 +1,53 @@
+import joblib
+import pandas as pd
+from fastapi import FastAPI
+from fastapi.encoders import jsonable_encoder
+from loguru import logger 
+from pydantic import BaseModel
+
+app = FastAPI()
+
+class Diabetes_measures(BaseModel):
+    Pregnancies: int = 6 # Number of times pregnant 
+    Glucose: int = (
+        148 # Plasma glucose concentration a 2 hours in an oral glucose tolerance test
+    )
+    BloodPressure: int = 72 # Diastolic blood pressure (mm Hg)
+    SkinThickness: int = 35 # Triceps skin fold thickness (mm)
+    Insulin: int = 0 # 2-Hour serum insulin
+    BMI: float = 33.6 # Body mass index
+    DiabetesPedigreeFunction: float = 0.627
+    Age: int = 50 # Age (years)
+
+model = joblib.load('./models/model.pkl')
+
+@app.get("/")
+def check_health():
+    return {"Status": "Hello World!"}
+
+cache = {}
+
+@app.post("/predict")
+def predict(data: Diabetes_measures):
+    logger.info('Making predictions...')
+    logger.info(data)
+    logger.info(jsonable_encoder(data))
+    logger.info(pd.DataFrame(jsonable_encoder(data), index=[0]))
+    result = model.predict(pd.DataFrame(jsonable_encoder(data), index=[0]))[0]
+
+    return {'result':  ['Normal', 'Diabetes'][result]}
+
+@app.post("/predict_cache")
+def predict_cache(data: Diabetes_measures):
+    if (str(data)) in cache:
+        logger.info("Getting result from cache!")
+        return cache[str(data)]
+    else:
+        logger.info('Making preidictions...')
+        logger.info(data)
+        logger.info(jsonable_encoder(data))
+        logger.info(pd.DataFrame(jsonable_encoder(data), index=[0]))
+        result = model.predict(pd.DataFrame(jsonable_encoder(data), index=[0]))[0]
+        cache[str(data)] = ["Normal", "Diabetes"][result]
+
+        return {"result": ['Normal', "Diabetes"][result]}

models/model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1f78c7d34d3e5f7821e9dc329faba664c092fc2f5dbcf307e2b484c978e60ee3
+size 37953

requirements.txt

@@ -0,0 +1,7 @@
+pandas
+numpy
+loguru
+fastapi == 0.111.0
+uvicorn
+urllib3
+catboost