Update paths

README.md

@@ -30,7 +30,7 @@ Save the data files locally in the `training_data` folder to ensure they are acc
 To run the app in a Docker container:
 
 ```bash
-docker-compose up
+docker-compose up --build
 ```
 
 This will build and start the containerized application, exposing it on `http://localhost:8000`.

app/app.py

@@ -1,10 +1,8 @@
 from fastapi import FastAPI
 from fastapi.responses import JSONResponse
-
-import plotly.io as pio
-import plotly.express as px
 from fastapi.middleware.cors import CORSMiddleware
-from main import main
+from main import getLapPlot, getPacketPlot 
+import plotly.io as pio
 
 app = FastAPI()
 
@@ -18,18 +16,21 @@
     allow_headers=["*"],
 )
 
-
 @app.get("/")
 async def read_root():
     return {"message": "Hello, World!"}
 
-
-@app.get("/plot")
-async def get_plot():
-    fig = main()
+@app.get("/lap_plot")
+async def get_lap_plot():
+    fig = getLapPlot()
     graphJSON = pio.to_json(fig)
     return JSONResponse(content=graphJSON)
 
+@app.get("/packet_plot")
+async def get_packet_plot():
+    fig = getPacketPlot()
+    graphJSON = pio.to_json(fig)
+    return JSONResponse(content=graphJSON)
 
 if __name__ == "__main__":
     import uvicorn

app/main.py

@@ -18,7 +18,6 @@ def cleanLapData(df: pd.DataFrame):
     df = df.dropna(subset=["averagepackCurrent"])
     return df
 
-
 def cleanPacketData(df: pd.DataFrame):
     # TODO: Add more cleaning steps when packet structure is finalized.
     #drop columns with null or empty values
@@ -55,12 +54,18 @@ def analyzeLapData(df: pd.DataFrame):
     print(cleanedDF.head())
     return generateCorrelationMatrix(cleanedDF)
 
-def main():
-    # lapDataDF = pd.read_feather(lapTrainingDataPath)
-    # return analyzeLapData(lapDataDF)
+def getLapPlot():
+    lapDataDF = pd.read_feather(lapTrainingDataPath)
+    return analyzeLapData(lapDataDF)
+
+def getPacketPlot():
     packetDataDF = pd.read_feather(packetTrainingDataPath)
     return analyzePacketData(packetDataDF)
 
+def main():
+    getLapPlot()
+    getPacketPlot()
+
 if __name__ == "__main__":
     main()
 

app/scripts/generateCSV.py

@@ -1,34 +1,48 @@
+from typing import List
 import pandas as pd
 import json
 import os
 
-from pathnames import LAP_JSON_PATH, PACKET_JSON_PATH, TRAINING_DATA_FOLDER
+from pathnames import LAP_JSON_PATH, PACKET_JSON_PATH, TRAINING_DATA_FOLDER, LAP_FEATHER_PATH, PACKET_FEATHER_PATH
 
 PACKET_CSV_PATH=f'{TRAINING_DATA_FOLDER}/Elysia.Packets.csv'
 LAP_CSV_PATH=f'{TRAINING_DATA_FOLDER}/Elysia.Laps.csv'
 
-def packetJSONToCSV(packetTrainingDF: pd.DataFrame):
-  packetTrainingDF.to_csv(PACKET_CSV_PATH)
-
-def lapJSONToCSV(lapTrainingDF: pd.DataFrame):
-  lapTrainingDF.to_csv(LAP_CSV_PATH)
-
-def main():
+def generatePacketCSV():
   if os.path.exists(PACKET_CSV_PATH):
     print(f"{PACKET_CSV_PATH} already exists. Delete them if you want to regenerate them.")
   else:
-    with open(LAP_JSON_PATH) as json_file:
-      data = json.load(json_file)
-      lapTrainingDF = pd.json_normalize(data)   
-      lapJSONToCSV(lapTrainingDF)
+    data = None
+    if(os.path.exists(PACKET_FEATHER_PATH)):
+        data = pd.read_feather(PACKET_FEATHER_PATH)
+    elif os.path.exists(PACKET_JSON_PATH):
+      with open(PACKET_JSON_PATH) as json_file:
+        jsonData = json.load(json_file)
+        data = pd.json_normalize(jsonData)   
+    if(data is not None):
+      data.to_csv(PACKET_CSV_PATH)
+    else:
+      print(f"Neither {PACKET_JSON_PATH} nor {PACKET_FEATHER_PATH} exists. Please generate them first or check that you are running this in the root.")
 
+def generateLapCSV():
   if(os.path.exists(LAP_CSV_PATH)):
     print(f"{LAP_CSV_PATH} already exists. Delete them if you want to regenerate them.")
   else:
-    with open(PACKET_JSON_PATH) as json_file:
-      data = json.load(json_file)
-      packetTrainingDF = pd.json_normalize(data)   
-      packetJSONToCSV(packetTrainingDF)
+    data = None
+    if(os.path.exists(LAP_FEATHER_PATH)):
+        data = pd.read_feather(LAP_FEATHER_PATH)
+    elif(os.path.exists(LAP_JSON_PATH)):
+      with open(LAP_JSON_PATH) as json_file:
+        jsonData = json.load(json_file)
+        data = pd.json_normalize(jsonData)   
+    if(data is not None):
+      data.to_csv(LAP_CSV_PATH)
+    else:
+      print(f"Neither {LAP_JSON_PATH} nor {LAP_FEATHER_PATH} exists. Please generate them first or check that you are running this in the root.")
+
+def main():
+  generateLapCSV()
+  generatePacketCSV()
 
 if __name__ == "__main__":
   main()

app/scripts/pathnames.py

@@ -1,3 +1,5 @@
 TRAINING_DATA_FOLDER = "./training_data"
 LAP_JSON_PATH=f"{TRAINING_DATA_FOLDER}/Elysia.Laps.json"
-PACKET_JSON_PATH=f"{TRAINING_DATA_FOLDER}/Elysia.Packets.json"
+PACKET_JSON_PATH=f"{TRAINING_DATA_FOLDER}/Elysia.Packets.json"
+LAP_FEATHER_PATH=f"{TRAINING_DATA_FOLDER}/Elysia.Laps.feather"
+PACKET_FEATHER_PATH=f"{TRAINING_DATA_FOLDER}/Elysia.Packets.feather"

docker-compose.yml

@@ -1,7 +1,8 @@
 services:
-  my-python-app:
-    build: .
-    image: my-python-app
+  ml-service:
+    build:
+      context: .
+    image: ml-service
     ports:
       - "8000:8000"
     volumes: