# main.py
from fastapi import FastAPI, File, UploadFile
from transformers import PaliGemmaProcessor, PaliGemmaForConditionalGeneration
from transformers.image_utils import load_image
import torch
from io import BytesIO
import os
from dotenv import load_dotenv
from PIL import Image

from huggingface_hub import login

# Load environment variables
load_dotenv()

# Set the cache directory to a writable path
os.environ["TORCHINDUCTOR_CACHE_DIR"] = "/tmp/torch_inductor_cache"

token = os.getenv("huggingface_ankit")
# Login to the Hugging Face Hub
login(token)

app = FastAPI()

model_id = "google/paligemma2-3b-mix-448"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).to('cuda')
processor = PaliGemmaProcessor.from_pretrained(model_id)

def predict(image):
    prompt = "<image> ocr"
    model_inputs = processor(text=prompt, images=image, return_tensors="pt").to('cuda')
    input_len = model_inputs["input_ids"].shape[-1]
    with torch.inference_mode():
        generation = model.generate(**model_inputs, max_new_tokens=200)
    torch.cuda.empty_cache()
    decoded = processor.decode(generation[0], skip_special_tokens=True) #[len(prompt):].lstrip("\n")
    return decoded

@app.post("/extract_text")
async def extract_text(file: UploadFile = File(...)):
    image = Image.open(BytesIO(await file.read())).convert("RGB")  # Ensure it's a valid PIL image
    text = predict(image)
    return {"extracted_text": text}

@app.post("/batch_extract_text")
async def batch_extract_text(files: list[UploadFile] = File(...)):
    if len(files) > 20:
        return {"error": "A maximum of 20 images can be processed at a time."}
    
    images = [Image.open(BytesIO(await file.read())).convert("RGB") for file in files]
    prompts = ["OCR"] * len(images)
    
    model_inputs = processor(text=prompts, images=images, return_tensors="pt").to(torch.bfloat16).to(model.device)
    input_len = model_inputs["input_ids"].shape[-1]
    
    with torch.inference_mode():
        generations = model.generate(**model_inputs, max_new_tokens=200, do_sample=False)
    torch.cuda.empty_cache()
    extracted_texts = [processor.decode(generations[i], skip_special_tokens=True) for i in range(len(images))]
    
    return {"extracted_texts": extracted_texts}
    
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)