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import spaces
import time
from threading import Thread
import gradio as gr
import torch
from PIL import Image
from transformers import AutoProcessor
from llava.constants import (
IMAGE_TOKEN_INDEX,
DEFAULT_IMAGE_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IM_END_TOKEN,
IMAGE_PLACEHOLDER,
)
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import (
process_images,
tokenizer_image_token,
get_model_name_from_path,
)
from io import BytesIO
import requests
import os
from conversation import Conversation, SeparatorStyle
model_id = "ytu-ce-cosmos/Turkish-LLaVA-v0.1"
disable_torch_init()
model_name = get_model_name_from_path(model_id)
tokenizer, model, image_processor, context_len = load_pretrained_model(
model_id, None, model_name
)
def load_image(image_file):
if image_file.startswith("http") or image_file.startswith("https"):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert("RGB")
elif os.path.exists(image_file):
image = Image.open(image_file).convert("RGB")
else:
raise FileNotFoundError(f"Image file {image_file} not found.")
return image
def infer_single_image(model_id, image_file, prompt):
image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
if IMAGE_PLACEHOLDER in prompt:
if model.config.mm_use_im_start_end:
prompt = re.sub(IMAGE_PLACEHOLDER, image_token_se, prompt)
else:
prompt = re.sub(IMAGE_PLACEHOLDER, DEFAULT_IMAGE_TOKEN, prompt)
else:
if model.config.mm_use_im_start_end:
prompt = image_token_se + "\n" + prompt
else:
prompt = DEFAULT_IMAGE_TOKEN + "\n" + prompt
conv = Conversation(
system="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nSen bir yapay zeka asistanısın. Kullanıcı sana bir görev verecek. Amacın görevi olabildiğince sadık bir şekilde tamamlamak. Görevi yerine getirirken adım adım düşün ve adımlarını gerekçelendir.""",
roles=("<|start_header_id|>user<|end_header_id|>\n\n", "<|start_header_id|>assistant<|end_header_id|>\n\n"),
version="llama3",
messages=[],
offset=0,
sep_style=SeparatorStyle.MPT,
sep="<|eot_id|>",
)
conv.append_message(conv.roles[0], prompt)
conv.append_message(conv.roles[1], None)
full_prompt = conv.get_prompt()
print("full prompt: ", full_prompt)
image = load_image(image_file)
image_tensor = process_images(
[image],
image_processor,
model.config
).to(model.device, dtype=torch.float16)
input_ids = (
tokenizer_image_token(full_prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
.unsqueeze(0)
.cuda()
)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
image_sizes=[image.size],
do_sample=False,
max_new_tokens=512,
use_cache=True,
)
output = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
return output
@spaces.GPU
def bot_streaming(message, history):
print(message)
if message["files"]:
if type(message["files"][-1]) == dict:
image = message["files"][-1]["path"]
else:
image = message["files"][-1]
else:
for hist in history:
if type(hist[0]) == tuple:
image = hist[0][0]
try:
if image is None:
gr.Error("You need to upload an image for LLaVA to work.")
except NameError:
gr.Error("You need to upload an image for LLaVA to work.")
prompt = message['text']
result = infer_single_image(model_id, image, prompt)
yield result
chatbot = gr.Chatbot(scale=1)
chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False)
with gr.Blocks(fill_height=True) as demo:
gr.ChatInterface(
fn=bot_streaming,
title="LLaVA Llama-3-8B",
examples=[{"text": "Çiçeğin üzerinde ne var?", "files": ["./bee.jpg"]},
{"text": "Bu tatlı nasıl yapılır?", "files": ["./baklava.png"]}],
description="",
stop_btn="Stop Generation",
multimodal=True,
textbox=chat_input,
chatbot=chatbot,
)
demo.queue(api_open=False)
demo.launch(show_api=False, share=False)
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