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# 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) |