app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import torch
from torch.nn.functional import softmax
import re
from predictor import predict, batch_predict  # Assuming batch_predict is in predictor module

app = FastAPI(
    title="Contact Information Detection API",
    description="API for detecting contact information in text, great thanks to xxparthparekhxx/ContactShieldAI for the model",
    version="1.0.0",
    docs_url="/"
)

def preprocess_text(text):
    # Remove all punctuation except for @ and . which are often used in email addresses
    return re.sub(r'[^\w\s@.]', '', text)

class TextInput(BaseModel):
    text: str

class BatchTextInput(BaseModel):
    texts: list[str]

def check_regex_patterns(text):
    patterns = [
        r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',  # Email
        r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b',  # Phone number
        r'\b\d{5}(?:[-\s]\d{4})?\b',  # ZIP code
        r'\b\d+\s+[\w\s]+(?:street|st|avenue|ave|road|rd|highway|hwy|square|sq|trail|trl|drive|dr|court|ct|park|parkway|pkwy|circle|cir|boulevard|blvd)\b\s*(?:[a-z]+\s*\d{1,3})?(?:,\s*(?:apt|bldg|dept|fl|hngr|lot|pier|rm|ste|unit|#)\s*[a-z0-9-]+)?(?:,\s*[a-z]+\s*[a-z]{2}\s*\d{5}(?:-\d{4})?)?',  # Street address
        r'(?:http|https)://(?:www\.)?[a-zA-Z0-9-]+\.[a-zA-Z]{2,}(?:/[^\s]*)?'  # Website URL
    ]
    
    for pattern in patterns:
        if re.search(pattern, text, re.IGNORECASE):
            return True
    return False

@app.post("/detect_contact", summary="Detect contact information in text")
async def detect_contact(input: TextInput):
    try:
        preprocessed_text = preprocess_text(input.text)
        
        # First, check with regex patterns
        if check_regex_patterns(preprocessed_text):
            return {
                "text": input.text,
                "is_contact_info": True,
                "method": "regex"
            }
        
        # If no regex patterns match, use the model
        is_contact = predict(preprocessed_text)
        return {
            "text": input.text,
            "is_contact_info": is_contact == 1,
            "method": "model"
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/batch_detect_contact", summary="Detect contact information in batch of texts")
async def batch_detect_contact(inputs: BatchTextInput):
    try:
        # Preprocess all texts
        preprocessed_texts = [preprocess_text(text) for text in inputs.texts]

        # First, use regex to check patterns
        regex_results = [check_regex_patterns(text) for text in preprocessed_texts]


        # For texts where regex doesn't detect anything, use the model
        texts_for_model = [text for text, regex_match in zip(preprocessed_texts, regex_results) if not regex_match]
        if texts_for_model:
            model_results = batch_predict(texts_for_model)
        else:
            model_results = []

        # Prepare final results
        results = []
        model_idx = 0
        for i, text in enumerate(preprocessed_texts):
            if regex_results[i]:
                results.append({
                    "text": inputs.texts[i],
                    "is_contact_info": True,
                    "method": "regex"
                })
            else:
                is_contact = model_results[model_idx]
                results.append({
                    "text": inputs.texts[i],
                    "is_contact_info": bool(is_contact),  # Convert numpy bool
                    "method": "model"
                })
                model_idx += 1

        return results
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))