Update main.py

main.py

@@ -1,31 +1,107 @@
-from fastapi import FastAPI
-
-from fastapi.responses import HTMLResponse, FileResponse
-
 import fastf1
 import pandas as pd
+from fastapi import FastAPI
+from fastapi.responses import FileResponse, HTMLResponse
+from pydantic import BaseModel
 
 app = FastAPI()
 
+
+# class Laps(BaseModel):
+#     Time: pd.Timedelta
+#     Driver: str
+#     DriverNumber: str
+#     LapTime: pd.Timedelta
+#     LapNumber: int
+#     Stint: int
+#     PitOutTime: pd.Timedelta
+#     PitInTime: pd.Timedelta
+#     Sector1Time: pd.Timedelta
+#     Sector2Time: pd.Timedelta
+#     Sector3Time: pd.Timedelta
+#     Sector1SessionTime: pd.Timedelta
+#     Sector2SessionTime: pd.Timedelta
+#     Sector3SessionTime: pd.Timedelta
+#     SpeedI1: float
+#     SpeedI2: float
+#     SpeedFL: float
+#     SpeedST: float
+#     IsPersonalBest: bool
+#     Compound: str
+#     TyreLife: float
+#     FreshTyre: bool
+#     Team: str
+#     LapStartTime: pd.Timedelta
+#     LapStartDate: pd.Timestamp
+#     TrackStatus: str
+#     IsAccurate: bool
+#     # https://stackoverflow.com/questions/65412984/using-pandas-data-frame-as-a-type-in-pydantic
+
+#     class Config:
+#         arbitrary_types_allowed = True
+
+
+class Laps(BaseModel):
+    Time: pd.core.frame.DataFrame
+    Driver: pd.core.frame.DataFrame
+    DriverNumber: pd.core.frame.DataFrame
+    LapTime: pd.core.frame.DataFrame
+    LapNumber: pd.core.frame.DataFrame
+    Stint: pd.core.frame.DataFrame
+    PitOutTime: pd.core.frame.DataFrame
+    PitInTime: pd.core.frame.DataFrame
+    Sector1Time: pd.core.frame.DataFrame
+    Sector2Time: pd.core.frame.DataFrame
+    Sector3Time: pd.core.frame.DataFrame
+    Sector1SessionTime: pd.core.frame.DataFrame
+    Sector2SessionTime: pd.core.frame.DataFrame
+    Sector3SessionTime: pd.core.frame.DataFrame
+    SpeedI1: pd.core.frame.DataFrame
+    SpeedI2: pd.core.frame.DataFrame
+    SpeedFL: pd.core.frame.DataFrame
+    SpeedST: pd.core.frame.DataFrame
+    IsPersonalBest: pd.core.frame.DataFrame
+    Compound: pd.core.frame.DataFrame
+    TyreLife: pd.core.frame.DataFrame
+    FreshTyre: pd.core.frame.DataFrame
+    Team: pd.core.frame.DataFrame
+    LapStartTime: pd.core.frame.DataFrame
+    LapStartDate: pd.core.frame.DataFrame
+    TrackStatus: pd.core.frame.DataFrame
+    IsAccurate: pd.core.frame.DataFrame
+    # https://stackoverflow.com/questions/65412984/using-pandas-data-frame-as-a-type-in-pydantic
+
+    class Config:
+        arbitrary_types_allowed = True
+
+
 @app.get("/")
-def read_root():
-    return {"Hello": "World!"}
+async def root():
+    return {"message": "Hello World"}
+
 
-@app.get("/{year}/{race}/{session}")
-async def get_data(year: int, race: int, session: str):
+# create a function that returns laps dataframe given year, race ,session from fastf1
+
+@app.get("/{year}/{race}/{session}", response_model=None)
+async def get_data(year: int, race: int, session: str) -> any:
     f1session = fastf1.get_session(year, race, session)
     f1session.load(telemetry=False, weather=False, messages=False)
     # Load all laps with telemetry
     laps = f1session.laps
-    laps['Sector1Time'] = laps['Sector1Time'].dt.total_seconds()
-    laps['Sector2Time'] = laps['Sector2Time'].dt.total_seconds()
-    laps['Sector3Time'] = laps['Sector3Time'].dt.total_seconds()
-    laps['LapTime_in_seconds'] = laps['LapTime'].dt.total_seconds()
-    laps['laptime_sum_sectortimes'] = laps.Sector1Time + \
-        laps.Sector2Time + laps.Sector3Time
-
-    # convert laps to csv
-    laps.to_csv('laps.csv', index=False)
+    # laps['Sector1Time'] = laps['Sector1Time'].dt.total_seconds()
+    # laps['Sector2Time'] = laps['Sector2Time'].dt.total_seconds()
+    # laps['Sector3Time'] = laps['Sector3Time'].dt.total_seconds()
+    # laps['LapTime_in_seconds'] = laps['LapTime'].dt.total_seconds()
+    # laps['laptime_sum_sectortimes'] = laps.Sector1Time + \
+    #     laps.Sector2Time + laps.Sector3Time
+
+    # # convert laps to csv
+    # laps.to_csv('laps.csv', index=False)
     # return laps
+
+    # in laps replace NaN with ""
+    laps = laps.fillna("")
+    return {"laps": laps.to_dict(orient='records')}
+
     # return FileResponse(path='laps.csv', filename='laps.csv')
-    return FileResponse(path='laps.csv', filename='laps.csv')
+    # return HTMLResponse(content=laps.to_html(), status_code=200)