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from fastapi import FastAPI, Body, Depends
from typing import Dict
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel, PeftConfig
from fastapi.middleware.cors import CORSMiddleware
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=['*'],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"]
)
def load_model():
peft_model_id = "ANWAR101/lora-bart-base-youtube-cnn"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, peft_model_id)
return model , tokenizer
@app.post("/summarize")
async def summarize(data: Dict[str, str] = Body(...)):
"""Summarize a text using the loaded Peft model."""
model , tokenizer = load_model()
text = data.get("text")
# Check for missing text
if not text:
return {"error": "Missing text in request body"}, 400
# Preprocess the text
inputs = tokenizer(text, truncation=True, return_tensors="pt")
# Generate summary using the model
outputs = model.generate(
**inputs, max_length=300, min_length=50, do_sample=True, num_beams=3,
no_repeat_ngram_size=2, temperature=0.6, length_penalty=1.0
)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
response = {"summary": summary}
return response |