from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import re

# Initialize FastAPI app
app = FastAPI(
    title="Email Classification API",
    version="1.0.0",
    description="Classifies support emails into categories and masks personal information.",
    docs_url="/docs",
    redoc_url="/redoc"
)

# Load pre-trained model
model = joblib.load("model.joblib")

# Input schema
class EmailInput(BaseModel):
    input_email_body: str

# Updated PII Masking Function (fixes Aadhaar vs Card and name misclassifications)
def mask_and_store_all_pii(text):
    text = str(text)
    pii_map = {}
    entity_list = []

    patterns = {
        "email": r"\b[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+\b",
        "phone_number": r"\+?\d[\d\s\-]{7,14}\d",
        "dob": r"\b\d{1,2}[\/\-\.\s]\d{1,2}[\/\-\.\s]\d{2,4}\b",
        "aadhar_num": r"\b\d{4}[ -]?\d{4}[ -]?\d{4}\b(?!\d)",
        "credit_debit_no": r"\b(?:\d[ -]*?){13,19}\b",
        "cvv_no": r"(?i)\b(?:cvv[:\s\-]*)?(\d{3,4})\b",
        "expiry_no": r"\b(0[1-9]|1[0-2])[\/\-]\d{2,4}\b",
        # Avoid naive full name pattern to prevent false positives like 'Dear Sir'
    }

    for label, pattern in patterns.items():
        for match in re.finditer(pattern, text):
            original = match.group()
            start, end = match.start(), match.end()
            placeholder = f"[{label}_{len(pii_map):03d}]"
            if original not in text:
                continue
            pii_map[placeholder] = original
            entity_list.append({
                "position": [start, end],
                "classification": label,
                "entity": original
            })
            text = text.replace(original, placeholder, 1)

    return text, pii_map, entity_list

# Restore PII
def restore_pii(masked_text, pii_map):
    restored = masked_text
    for placeholder, original in pii_map.items():
        restored = restored.replace(placeholder, original)
    return restored

# Classification Endpoint
@app.post("/classify")
def classify_email(data: EmailInput):
    raw_text = data.input_email_body

    # Masking
    masked_text, pii_map, entity_list = mask_and_store_all_pii(raw_text)

    # Prediction
    predicted_category = model.predict([masked_text])[0]

    # Response format
    return {
        "input_email_body": raw_text,
        "list_of_masked_entities": entity_list,
        "masked_email": masked_text,
        "category_of_the_email": predicted_category
    }

# Health check endpoint
@app.get("/")
def root():
    return {"message": "Email Classification API is running."}