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d8bb4c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import T5Tokenizer, T5ForConditionalGeneration
import os
app = FastAPI(title="Clickbait Detector API")
# Enable CORS for Chrome Extension
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Global variables for models
tokenizer = None
model = None
t5_tokenizer = None
t5_model = None
device = None
@app.on_event("startup")
async def load_models():
"""Load models on startup"""
global tokenizer, model, t5_tokenizer, t5_model, device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {device}")
# Load DistilBERT
print("Loading DistilBERT model...")
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained(
"./clickbait_detector_model"
)
model.to(device)
model.eval()
print("✓ DistilBERT loaded")
# Load T5
print("Loading T5 model...")
t5_tokenizer = T5Tokenizer.from_pretrained("t5-base")
t5_model = T5ForConditionalGeneration.from_pretrained(
"./t5_clickbait_rewriter_finetuned"
)
t5_model.to(device)
t5_model.eval()
print("✓ T5 loaded")
class HeadlineRequest(BaseModel):
headline: str
@app.get("/")
def root():
return {
"name": "Clickbait Detector API",
"version": "1.0.0",
"status": "online",
"endpoints": {
"/detect": "POST - Detect if headline is clickbait",
"/rewrite": "POST - Rewrite clickbait to neutral",
"/analyze": "POST - Detect + Rewrite in one call"
}
}
@app.post("/detect")
def detect_clickbait(request: HeadlineRequest):
"""Detect if headline is clickbait"""
try:
inputs = tokenizer(
request.headline,
return_tensors='pt',
padding=True,
truncation=True,
max_length=128
).to(device)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)
prediction = torch.argmax(probs, dim=1).item()
confidence = probs[0][prediction].item()
return {
"headline": request.headline,
"is_clickbait": bool(prediction),
"confidence": float(confidence),
"label": "clickbait" if prediction else "neutral"
}
except Exception as e:
return {"error": str(e)}
@app.post("/rewrite")
def rewrite_headline(request: HeadlineRequest):
"""Rewrite clickbait headline to neutral"""
try:
prompt = f"rewrite clickbait to neutral: {request.headline}"
inputs = t5_tokenizer(
prompt,
return_tensors='pt',
max_length=128,
truncation=True
).to(device)
with torch.no_grad():
outputs = t5_model.generate(
inputs['input_ids'],
max_length=64,
num_beams=5,
early_stopping=True
)
neutral = t5_tokenizer.decode(outputs[0], skip_special_tokens=True)
return {
"original": request.headline,
"rewritten": neutral
}
except Exception as e:
return {"error": str(e)}
@app.post("/analyze")
def analyze_headline(request: HeadlineRequest):
"""Detect and rewrite in one call"""
try:
detection = detect_clickbait(request)
if detection.get("is_clickbait"):
rewrite = rewrite_headline(request)
return {
**detection,
"rewritten": rewrite.get("rewritten")
}
else:
return {
**detection,
"rewritten": request.headline,
"message": "Headline is already neutral"
}
except Exception as e:
return {"error": str(e)}
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