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import torch
import numpy as np
import spaces
from PIL import Image, ImageDraw, ImageFont
from transformers import AutoConfig, AutoModelForCausalLM, LlavaForConditionalGeneration, LlavaNextForConditionalGeneration, LlavaNextProcessor, AutoProcessor, PaliGemmaForConditionalGeneration
from transformers import CLIPProcessor, CLIPModel
from janus.models import MultiModalityCausalLM, VLChatProcessor
@spaces.GPU(duration=120)
def set_dtype_device(model, precision=16):
dtype = (torch.bfloat16 if torch.cuda.is_available() else torch.float16) if precision==16 else (torch.bfloat32 if torch.cuda.is_available() else torch.float32)
cuda_device = 'cuda' if torch.cuda.is_available() else 'cpu'
if torch.cuda.is_available():
model = model.to(dtype).cuda()
else:
torch.set_default_device("cpu")
model = model.to(dtype)
return model, dtype, cuda_device
class Model_Utils:
def __init__(self):
pass
@spaces.GPU(duration=120)
def prepare_inputs(self):
raise NotImplementedError
@spaces.GPU(duration=120)
def generate_outputs(self):
raise NotImplementedError
class Clip_Utils(Model_Utils):
def __init__(self):
self.edge = 224
super().__init__()
def init_Clip(self):
self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
self.processor.feature_extractor.size = {"height": self.edge, "width": self.edge}
@spaces.GPU(duration=120)
def prepare_inputs(self, question_lst, image):
image = Image.fromarray(image)
# print("image_size: ", image.size)
inputs = self.processor(text=question_lst, images=image, return_tensors="pt", padding=True)
return inputs
class Janus_Utils(Model_Utils):
def __init__(self):
super().__init__()
def init_Janus(self, num_params="1B"):
model_path = f"deepseek-ai/Janus-Pro-{num_params}"
config = AutoConfig.from_pretrained(model_path)
language_config = config.language_config
language_config._attn_implementation = 'eager'
self.vl_gpt = AutoModelForCausalLM.from_pretrained(model_path,
language_config=language_config,
trust_remote_code=True,
ignore_mismatched_sizes=True,
)
self.vl_gpt, self.dtype, self.cuda_device = set_dtype_device(self.vl_gpt)
self.vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
self.tokenizer = self.vl_chat_processor.tokenizer
return self.vl_gpt, self.tokenizer
@spaces.GPU(duration=120)
def prepare_inputs(self, question, image):
conversation = [
{
"role": "<|User|>",
"content": f"<image_placeholder>\n{question}",
"images": [image],
},
{"role": "<|Assistant|>", "content": ""},
]
pil_images = [Image.fromarray(image)]
prepare_inputs = self.vl_chat_processor(
conversations=conversation, images=pil_images, force_batchify=True
).to(self.cuda_device, dtype=self.dtype)
return prepare_inputs
@spaces.GPU(duration=120)
def generate_inputs_embeddings(self, prepare_inputs):
return self.vl_gpt.prepare_inputs_embeds(**prepare_inputs)
@spaces.GPU(duration=120)
def generate_outputs(self, inputs_embeds, prepare_inputs, temperature, top_p, with_attn=False):
outputs = self.vl_gpt.language_model.generate(
inputs_embeds=inputs_embeds,
attention_mask=prepare_inputs.attention_mask,
pad_token_id=self.tokenizer.eos_token_id,
bos_token_id=self.tokenizer.bos_token_id,
eos_token_id=self.tokenizer.eos_token_id,
max_new_tokens=512,
do_sample=False if temperature == 0 else True,
use_cache=True,
temperature=temperature,
top_p=top_p,
return_dict_in_generate=True,
output_attentions=True
)
return outputs
class LLaVA_Utils(Model_Utils):
def __init__(self):
super().__init__()
def init_LLaVA(self):
# model_path = "llava-hf/llava-1.5-7b-hf"
model_path = "llava-hf/llava-v1.6-mistral-7b-hf"
config = AutoConfig.from_pretrained(model_path)
self.vl_gpt = LlavaNextForConditionalGeneration.from_pretrained(model_path,
low_cpu_mem_usage=True,
attn_implementation = 'eager',
output_attentions=True
)
self.vl_gpt, self.dtype, self.cuda_device = set_dtype_device(self.vl_gpt)
self.processor = LlavaNextProcessor.from_pretrained(model_path)
self.tokenizer = self.processor.tokenizer
return self.vl_gpt, self.tokenizer
@spaces.GPU(duration=120)
def prepare_inputs(self, question, image):
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": question},
{"type": "image"},
],
},
]
prompt = self.processor.apply_chat_template(conversation, add_generation_prompt=True)
pil_images = [Image.fromarray(image)]
prepare_inputs = self.processor(
images=pil_images, text=prompt, return_tensors="pt"
).to(self.cuda_device, dtype=self.dtype)
return prepare_inputs
@spaces.GPU(duration=120)
def generate_inputs_embeddings(self, prepare_inputs):
return self.vl_gpt.prepare_inputs_embeds(**prepare_inputs)
@spaces.GPU(duration=120)
def generate_outputs(self, prepare_inputs, temperature, top_p):
outputs = self.vl_gpt.generate(
**prepare_inputs,
max_new_tokens=512,
do_sample=False if temperature == 0 else True,
use_cache=True,
return_dict_in_generate=True,
output_attentions=True
)
return outputs
class ChartGemma_Utils(Model_Utils):
def __init__(self):
super().__init__()
def init_ChartGemma(self):
model_path = "ahmed-masry/chartgemma"
self.vl_gpt = PaliGemmaForConditionalGeneration.from_pretrained(
model_path,
torch_dtype=torch.float16,
attn_implementation="eager",
output_attentions=True
)
self.vl_gpt, self.dtype, self.cuda_device = set_dtype_device(self.vl_gpt)
self.processor = AutoProcessor.from_pretrained(model_path)
self.tokenizer = self.processor.tokenizer
return self.vl_gpt, self.tokenizer
@spaces.GPU(duration=120)
def prepare_inputs(self, question, image):
pil_image = Image.fromarray(image)
prepare_inputs = self.processor(
images=pil_image, text=[question], return_tensors="pt"
).to(self.cuda_device, dtype=self.dtype)
return prepare_inputs
@spaces.GPU(duration=120)
def generate_inputs_embeddings(self, prepare_inputs):
return self.vl_gpt.prepare_inputs_embeds(**prepare_inputs)
@spaces.GPU(duration=120)
def generate_outputs(self, prepare_inputs, temperature, top_p):
outputs = self.vl_gpt.generate(
**prepare_inputs,
max_new_tokens=512,
do_sample=False if temperature == 0 else True,
use_cache=True,
return_dict_in_generate=True,
output_attentions=True
)
return outputs
def add_title_to_image(image, title, font_size=50):
"""Adds a title above an image using PIL and textbbox()."""
img_width, img_height = image.size
# Create a blank image for title
title_height = font_size + 10 # Some padding
title_image = Image.new("RGB", (img_width, title_height), color=(255, 255, 255)) # White background
draw = ImageDraw.Draw(title_image)
# Load font
try:
font = ImageFont.truetype("arial.ttf", font_size) # Use Arial if available
except:
font = ImageFont.load_default() # Use default if Arial not found
# Get text size (updated for PIL >= 10)
text_bbox = draw.textbbox((0, 0), title, font=font)
text_width = text_bbox[2] - text_bbox[0]
text_height = text_bbox[3] - text_bbox[1]
# Center the title
text_position = ((img_width - text_width) // 2, (title_height - text_height) // 2)
draw.text(text_position, title, fill="black", font=font)
# Concatenate title with image
combined = Image.new("RGB", (img_width, img_height + title_height))
combined.paste(title_image, (0, 0)) # Place title at the top
combined.paste(image, (0, title_height)) # Place original image below
return combined
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