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Running
on
Zero
Running
on
Zero
# Necessary imports | |
import sys | |
import PIL.Image | |
import torch | |
import gradio as gr | |
import spaces | |
# Local imports | |
from src.config import device, model_name | |
from src.paligemma.model import load_model_and_processor | |
from src.logger import logging | |
from src.exception import CustomExceptionHandling | |
# Language dictionary | |
language_dict = { | |
"English": "en", | |
"Spanish": "es", | |
"French": "fr", | |
} | |
# Model and processor | |
model, processor = load_model_and_processor(model_name, device) | |
def caption_image(image: PIL.Image.Image, max_new_tokens: int, language: str) -> str: | |
""" | |
Generates a caption based on the given image using the model. | |
Args: | |
- image (PIL.Image.Image): The input image to be processed. | |
- max_new_tokens (int): The maximum number of new tokens to generate. | |
- language (str): The language of the generated caption. | |
Returns: | |
str: The generated caption text. | |
""" | |
try: | |
# Check if image is None | |
if not image: | |
gr.Warning("Please provide an image.") | |
# Prepare the inputs | |
language = language_dict[language] | |
prompt = f"<image>caption {language}" | |
model_inputs = ( | |
processor(text=prompt, images=image, return_tensors="pt") | |
.to(torch.bfloat16) | |
.to(device) | |
) | |
input_len = model_inputs["input_ids"].shape[-1] | |
# Generate the response | |
with torch.inference_mode(): | |
generation = model.generate( | |
**model_inputs, max_new_tokens=max_new_tokens, do_sample=False | |
) | |
generation = generation[0][input_len:] | |
decoded = processor.decode(generation, skip_special_tokens=True) | |
# Log the successful generation of the caption | |
logging.info("Caption generated successfully.") | |
# Return the generated caption | |
return decoded | |
# Handle exceptions that may occur during caption generation | |
except Exception as e: | |
# Custom exception handling | |
raise CustomExceptionHandling(e, sys) from e | |