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from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
import pandas as pd
import joblib
import os
router = APIRouter()
# Define paths for preprocessing objects and model
preprocessing_path = os.path.join("models", "preprocessing_objects.pkl")
model_path = os.path.join("models", "bail_reckoner_model.pkl")
# Load preprocessing objects and model with error handling
try:
preprocessing_objects = joblib.load(preprocessing_path)
if preprocessing_objects is None:
raise FileNotFoundError(f"Preprocessing objects file is empty or corrupted: {preprocessing_path}")
label_encoders = preprocessing_objects.get('label_encoders', {})
scaler = preprocessing_objects.get('scaler', None)
if not label_encoders:
raise KeyError("Label encoders are missing from the preprocessing objects.")
if not scaler:
raise KeyError("Scaler object is missing from the preprocessing objects.")
except FileNotFoundError as e:
raise HTTPException(status_code=500, detail=str(e))
except KeyError as e:
raise HTTPException(status_code=500, detail=f"Missing key in preprocessing objects: {str(e)}")
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error loading preprocessing objects: {str(e)}")
# Load the bail reckoner model
try:
model = joblib.load(model_path)
if model is None:
raise FileNotFoundError(f"Model file is empty or corrupted: {model_path}")
except FileNotFoundError as e:
raise HTTPException(status_code=500, detail=str(e))
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error loading model: {str(e)}")
# Define Pydantic model for input data
class BailInput(BaseModel):
statute: str
offense_category: str
penalty: str
imprisonment_duration_served: int
risk_of_escape: int
risk_of_influence: int
surety_bond_required: int
personal_bond_required: int
fines_applicable: int
served_half_term: int
risk_score: float
penalty_severity: float
@router.post("/predict-bail")
async def predict_bail(data: BailInput):
try:
# Convert input data to DataFrame for model prediction
user_input = pd.DataFrame([data.dict()])
# Apply label encoding to categorical columns
for col, encoder in label_encoders.items():
if col in user_input:
user_input[col] = encoder.transform(user_input[col])
# Scale the numerical columns
numerical_columns = ['imprisonment_duration_served', 'risk_score', 'penalty_severity']
user_input[numerical_columns] = scaler.transform(user_input[numerical_columns])
# Make the prediction
result = model.predict(user_input)
prediction = "Eligible for Bail" if result[0] == 1 else "Not Eligible for Bail"
return {"prediction": prediction}
except Exception as e:
raise HTTPException(status_code=400, detail=f"Error processing prediction request: {str(e)}")
@router.get("/")
async def root():
return {"message": "Bail Reckoner API is running."}
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