app/services/flan_t5_service.py

from transformers import T5ForConditionalGeneration, T5Tokenizer

class FlanT5Service:
    def __init__(self):
        self.model_name = "google/flan-t5-base"
        self.tokenizer = T5Tokenizer.from_pretrained(self.model_name)
        self.model = T5ForConditionalGeneration.from_pretrained(self.model_name)

    async def generate_response(self, prompt: str, max_length: int = 512) -> str:
        inputs = self.tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
        outputs = self.model.generate(
            **inputs,
            max_length=max_length,
            num_beams=4,
            temperature=0.7,
            top_p=0.9
        )
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)