app.py

import os
from fastapi import FastAPI
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
from pydantic import BaseModel

# Set writable cache directory inside the container
os.environ['SENTENCE_TRANSFORMERS_HOME'] = '/app/hf_home'
os.environ['TRANSFORMERS_CACHE'] = '/app/hf_home'

# Ensure the directory exists
os.makedirs(os.environ['TRANSFORMERS_CACHE'], exist_ok=True)

# Define base model and adapter model
base_model_name = "facebook/opt-2.7b"
adapter_name = "mynuddin/chatbot"  # Adapter model path or name

# Load base model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float16)

# Load PEFT adapter
model = PeftModel.from_pretrained(base_model, adapter_name)
model = model.to("cuda" if torch.cuda.is_available() else "cpu")  # Use GPU if available
model.eval()

app = FastAPI()

# Define Pydantic model for input
class PromptInput(BaseModel):
    prompt: str

@app.post("/generate")
def generate_text(input: PromptInput):
    prompt = input.prompt  # Access prompt from the request body

    # Format the prompt with specific style for your fine-tuned model
    inputs = tokenizer(f"### Prompt: {prompt}\n### Completion:", return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")

    # Generate the output
    with torch.no_grad():
        output = model.generate(**inputs, max_length=128)
    
    # Decode the output and remove special tokens
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

    return {"generated_query": generated_text}