Main.py

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+from fastapi import FastAPI,HTTPException
+import uvicorn
+
+# Create a FastAPI instance
+app = FastAPI()
+
+# Load the entire pipeline
+rfc_pipeline = joblib.load('./rfc_pipeline.joblib')
+encoder = joblib.load('./encoder.joblib')
+
+# Define a FastAPI instance ML model input schema
+class IncomePredictionInput(BaseModel):
+    age:                   int
+    gender:                object 
+    education:             object 
+    worker_class:          object 
+    marital_status:        object 
+    race:                  object 
+    is_hispanic:           object 
+    employment_commitment: object 
+    employment_stat:       int  
+    wage_per_hour:         int  
+    working_week_per_year: int  
+    industry_code:         int  
+    industry_code_main:    object 
+    occupation_code:       int  
+    occupation_code_main:  object 
+    total_employed:        int 
+    household_summary:     object 
+    vet_benefit:           int 
+    tax_status:            object 
+    gains:                 int 
+    losses:                int  
+    stocks_status:         int  
+    citizenship:           object 
+    importance_of_record:  float
+ 
+class IncomePredictionOutput(BaseModel):
+    income_prediction: str
+    prediction_probability: float   
+# Defining the root endpoint for the API
+@app.get("/")
+def index():
+    explanation = {
+        'message': "Welcome to the Income Iniquality Prediction App",
+        'description': "This API allows you to predict Income Iniquality based on Personal data.",
+    }
+    return explanation
+
+
+@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)