all files

encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:98baac762b1becb7d5b699f21576a27ebe7555a83826444f23d540dcfc7d01d1
+size 270

main.py

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+from fastapi import FastAPI,HTTPException
+from typing import Literal,List
+import uvicorn
+from pydantic import BaseModel
+import pandas as pd
+import os
+import pickle
+
+# setup
+SRC = os.path.abspath('./SRC')
+
+# Load the pipeline using pickle
+pipeline_path = os.path.join(SRC, 'rfc_pipeline.pkl')
+with open(pipeline_path, 'rb') as file:
+    rfc_pipeline = pickle.load(file)
+
+# Load the encoder using pickle
+encoder_path = os.path.join(SRC, 'encoder.pkl')
+with open(encoder_path, 'rb') as file:
+    encoder = pickle.load(file)
+
+app = FastAPI(
+    title= 'Income Classification FastAPI',
+    description='A FastAPI service to classify individuals based on income level using a trained machine learning model.',
+    version= '1.0.0'
+)
+
+class IncomePredictionInput(BaseModel):
+    age:                   int
+    gender:                str
+    education:             str
+    worker_class:          str
+    marital_status:        str
+    race:                  str
+    is_hispanic:           str
+    employment_commitment: str
+    employment_stat:       int
+    wage_per_hour:         int
+    working_week_per_year: int
+    industry_code:         int
+    industry_code_main:    str
+    occupation_code:       int
+    occupation_code_main:  str
+    total_employed:        int
+    household_summary:     str
+    vet_benefit:           int
+    tax_status:            str
+    gains:                 int
+    losses:                int
+    stocks_status:         int
+    citizenship:           str
+    importance_of_record:  float
+
+   
+class IncomePredictionOutput(BaseModel):
+    income_prediction: str
+    prediction_probability: float
+
+
+# get
+@app.get('/')
+def home():
+    return {
+        'message': 'Income Classification FastAPI',
+        'description': 'FastAPI service to classify individuals based on income level.',
+        'instruction': 'Click here (/docs) to access API documentation and test endpoints.'
+    }
+   
+
+# post
+@app.post('/classify', response_model=IncomePredictionOutput)
+def income_classification(income: IncomePredictionInput):
+    try:
+        df = pd.DataFrame([income.model_dump()])
+           
+        # Make predictions
+        prediction = rfc_pipeline.predict(df)
+        output = rfc_pipeline.predict_proba(df)
+
+        prediction_result = "Income over $50K" if prediction[0] == 1 else "Income under $50K"
+        return {"income_prediction": prediction_result, "prediction_probability": output[0][1]}
+
+
+    except Exception as e:
+        # Return error message and details if an exception occurs
+        error_detail = str(e)
+        raise HTTPException(status_code=500, detail=f"Error during classification: {error_detail}")
+
+
+if __name__ == '__main__':
+    uvicorn.run('main:app', reload=True)

rfc_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de6be7713061b3b93f04b7e4c3239216dc6f51b4b18ead0b60fce19c0cbe594e
+size 223631035