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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification
from torch.nn.functional import softmax
import re
app = FastAPI(
title="Contact Information Detection API",
description="API for detecting contact information in text",
version="1.0.0",
docs_url="/"
)
class ContactDetector:
def __init__(self):
cache_dir = "/app/model_cache"
self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base', cache_dir=cache_dir)
self.model = RobertaForSequenceClassification.from_pretrained('roberta-base', num_labels=2, cache_dir=cache_dir)
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model.to(self.device)
self.model.eval()
def detect_contact_info(self, text):
inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(self.device)
with torch.no_grad():
outputs = self.model(**inputs)
probabilities = softmax(outputs.logits, dim=1)
return probabilities[0][1].item() # Probability of contact info
def is_contact_info(self, text, threshold=0.45):
return self.detect_contact_info(text) > threshold
detector = ContactDetector()
class TextInput(BaseModel):
text: 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:
# First, check with regex patterns
if check_regex_patterns(input.text):
return {
"text": input.text,
"contact_probability": 1.0,
"is_contact_info": True,
"method": "regex"
}
# If no regex patterns match, use the model
probability = detector.detect_contact_info(input.text)
is_contact = detector.is_contact_info(input.text)
return {
"text": input.text,
"contact_probability": probability,
"is_contact_info": is_contact,
"method": "model"
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
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