initial commit

Dockerfile

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+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", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+import uvicorn
+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+from typing import Dict
+
+app = FastAPI()
+loaded_model = joblib.load('model.joblib', mmap_mode='r')
+
+
+class PredictRequest(BaseModel):
+    AG_X: float
+    AG_Y: float
+    AG_Z: float
+    Acc_X: float
+    Acc_Y: float
+    Acc_Z: float
+    Gravity_X: float
+    Gravity_Y: float
+    Gravity_Z: float
+    RR_X: float
+    RR_Y: float
+    RR_Z: float
+    RV_X: float
+    RV_Y: float
+    RV_Z: float
+    cos: float
+
+class PredictResponse(BaseModel):
+    activity: str
+
+
+
+@app.post('/predict', response_model=PredictResponse)
+def predict(data: PredictRequest):
+    features = [data.AG_X, data.AG_Y, data.AG_Z, data.Acc_X, data.Acc_Y, data.Acc_Z, data.Gravity_X, data.Gravity_Y,
+                data.Gravity_Z, data.RR_X, data.RR_Y, data.RR_Z, data.RV_X, data.RV_Y, data.RV_Z, data.cos]
+
+    predictions = loaded_model.predict([features])[0]
+
+
+    activities = ["Running", "Sitting", "Standing", "Walking", "DownStairs", "Upstairs"]
+    activity = activities[predictions]
+
+    return {'activity': activity}
+
+if __name__ == '__main__':
+    uvicorn.run(app, host='127.0.0.1', port=8000)

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e4fc919438deacc929f121e9d0fa42b7c30ca3647b910167d92c89b36fef4065
+size 588941763