main.py

from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the model and tokenizer
model_name = "shahzaib201/AI_OEL"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Pydantic model for input validation
class TextInput(BaseModel):
    text: str
    max_length: int = 150

# Initialize FastAPI app
app = FastAPI()

# Endpoint for text summarization
@app.post("/summarize_text")
async def summarize_text_endpoint(item: TextInput):
    # Tokenize the input text
    inputs = tokenizer(item.text, return_tensors="pt", max_length=1024, truncation=True)
    
    # Generate the summary
    summary_ids = model.generate(inputs.input_ids, max_length=item.max_length, num_beams=4, length_penalty=2.0, early_stopping=True)
    
    # Decode the generated summary
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    
    return {"summary": summary}