|
|
| from fastapi import FastAPI, HTTPException, File, UploadFile |
| from fastapi.responses import FileResponse |
| from app.services.preprocessing import data_quality, standardize_data_types, handle_missing_data, handle_outliers, generate_final_report, save_cleaned_data |
| import pandas as pd |
| import io |
| import os |
|
|
| app = FastAPI(title="Data Preprocessing") |
| os.makedirs("output", exist_ok=True) |
|
|
| @app.get("/") |
| async def root(): |
| return {"message": "Welcome to the Data Preprocessing API!"} |
|
|
| @app.post("/preprocess_data/") |
| async def upload_csv(upload_file: UploadFile = File(...)): |
| try: |
| if not upload_file.filename.endswith('.csv'): |
| raise HTTPException(status_code=400, detail="File must be in CSV format!") |
| content = await upload_file.read() |
| df = pd.read_csv(io.BytesIO(content), encoding_errors="replace") |
| if df.empty: |
| raise HTTPException(status_code=400, detail="File is empty, upload the correct file") |
|
|
| data_quality(df) |
| df = standardize_data_types(df) |
| df = handle_missing_data(df) |
| df = handle_outliers(df) |
|
|
| REPORT_PATH = "output/preprocessing_report.txt" |
| generate_final_report(df, REPORT_PATH) |
|
|
| CLEANED_DATA_PATH = "output/cleaned_dataset.csv" |
| save_cleaned_data(df, CLEANED_DATA_PATH) |
|
|
| return FileResponse(CLEANED_DATA_PATH, media_type="text/csv", filename="cleaned_dataset.csv") |
|
|
| except Exception as e: |
| raise HTTPException(status_code=400, detail=f"Error processing file: {str(e)}") |
|
|
