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# For app development
from fastapi import FastAPI, File, UploadFile
from typing import Annotated
from fastapi import FastAPI, Form, Depends
# Import the Response class from fastapi
from fastapi import Response
import pandas as pd
import uvicorn
from pydantic import BaseModel
# For data frame
import pandas as pd
# For loading pipeline
import joblib
# For controlling warnings
import warnings
warnings.filterwarnings('ignore')
# For system functions
from io import BytesIO
import os
# Pieline loading
with open("catboost_pipeline_4.job", "rb") as f:
pipe = joblib.load(f)
print(pipe)
# instantiating fastAPI object
app = FastAPI( title = "Machine Learning Classification API for predicting \
Income limit as Above Limit / Below Limit")
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
try:
content = await file.read()
df = pd.read_csv(BytesIO(content))
columns = ['age', 'gender', 'education', 'class', 'marital_status', 'race',
'is_hispanic', 'employment_commitment', 'employment_stat',
'wage_per_hour', 'working_week_per_year', 'industry_code',
'industry_code_main', 'occupation_code', 'occupation_code_main',
'total_employed', 'household_stat', 'household_summary', 'vet_benefit',
'tax_status', 'gains', 'losses', 'stocks_status', 'citizenship',
'mig_year', 'country_of_birth_own', 'country_of_birth_father',
'country_of_birth_mother', 'importance_of_record']
# Copying neccesary columns
id = df["ID"].copy()
print(id)
df = df[ columns ].copy()
print(df)
# Renaming column class
df.rename(columns={'class': 'class_'}, inplace=True)
# Predicting...
output = pipe.predict_proba(df)
print(output)
df["predicted_label"] = output.argmax(axis = -1)
mapping = {0: "Below Limit", 1: "Above Limit"}
df["predicted_label"] = [mapping[x] for x in df["predicted_label"]]
print(df["predicted_label"])
# Calculating confidence score
confidence_score = output.max(axis= -1)
df["confidence_score"] = f"{round( ( confidence_score[0] * 100 ) , 2) }%"
# Create a dataframe with a column named 'id'
df_final = pd.DataFrame(id, columns=['ID'])
print(df_final)
df_final["predicted_label"] = df["predicted_label"]
df_final["confidence_score"] = df["confidence_score"]
print(df_final)
print("****************")
# Convert the dataframe to a CSV string
df_csv = df_final.to_csv(index=False)
# Return the CSV string with the response class
return Response(content=df_csv, media_type="text/csv")
except Exception as e:
return {"error": str(e)} # return an error message if something goes wrong
if __name__ == "__main__":
uvicorn.run("main:app" , reload = True) |