FastAPI

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import pickle
+import pandas as pd
+
+# Load the saved model
+with open("model_and_key_components.pkl", "rb") as f:
+    components = pickle.load(f)
+dt_model = components['model']
+
+app = FastAPI()
+
+class IncomePredictionRequest(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_stat: str
+    household_summary: str
+    vet_benefit: int
+    tax_status: str
+    gains: int
+    losses: int
+    stocks_status: int
+    citizenship: str
+    mig_year: int
+    country_of_birth_own: str
+    importance_of_record: float
+
+   
+class IncomePredictionResponse(BaseModel):
+    income_prediction: str
+    prediction_probability: float
+
+@app.post("/predict/")
+async def predict_income(data: IncomePredictionRequest):
+    input_data = data.dict()
+    input_df = pd.DataFrame([input_data])
+    prediction = dt_model.predict(input_df)
+    prediction_proba = dt_model.predict_proba(input_df)
+    prediction_result = "Income over $50K" if prediction[0] == 1 else "Income under $50K"
+    return {"income_prediction": prediction_result, "prediction_probability": prediction_proba[0][1]}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)