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import os | |
import subprocess | |
# Install flash attention | |
subprocess.run( | |
"pip install flash-attn --no-build-isolation", | |
env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, | |
shell=True, | |
) | |
import copy | |
import spaces | |
import time | |
import torch | |
from threading import Thread | |
from typing import List, Dict, Union | |
import urllib | |
from urllib.parse import urlparse | |
from PIL import Image | |
import io | |
import pandas as pd | |
import datasets | |
import json | |
import requests | |
import gradio as gr | |
from transformers import AutoProcessor, TextIteratorStreamer | |
from transformers import Idefics2ForConditionalGeneration | |
DEVICE = torch.device("cuda") | |
MODELS = { | |
"idefics2-8b-chatty": Idefics2ForConditionalGeneration.from_pretrained( | |
"HuggingFaceM4/idefics2-8b-chatty", | |
torch_dtype=torch.bfloat16, | |
_attn_implementation="flash_attention_2", | |
trust_remote_code=True, | |
token=os.environ["HF_AUTH_TOKEN"], | |
).to(DEVICE), | |
} | |
PROCESSOR = AutoProcessor.from_pretrained( | |
"HuggingFaceM4/idefics2-8b", | |
token=os.environ["HF_AUTH_TOKEN"], | |
) | |
SYSTEM_PROMPT = [ | |
{ | |
"role": "system", | |
"content": [ | |
{ | |
"type": "text", | |
"text": "The following is a conversation between Idefics2, a highly knowledgeable and intelligent visual AI assistant created by Hugging Face, referred to as Assistant, and a human user called User. In the following interactions, User and Assistant will converse in natural language, and Assistant will do its best to answer Userβs questions. Assistant has the ability to perceive images and reason about the content of visual inputs. Assistant was built to be respectful, polite and inclusive. It knows a lot, and always tells the truth. When prompted with an image, it does not make up facts.", | |
}, | |
], | |
} | |
] | |
examples_path = os.path.dirname(__file__) | |
EXAMPLES = [ | |
[ | |
{ | |
"text": "What's in the image?", | |
"files": [f"{examples_path}/example_images/plant_bulb.webp"], | |
} | |
], | |
[ | |
{ | |
"text": "What's funny about this image?", | |
"files": [f"{examples_path}/example_images/pope_doudoune.webp"], | |
} | |
], | |
[ | |
{ | |
"text": "Why is this image cute", | |
"files": [ | |
f"{examples_path}/example_images/kittens-cats-pet-cute-preview.jpg" | |
], | |
} | |
], | |
[ | |
{ | |
"text": "Describe the image", | |
"files": [f"{examples_path}/example_images/baguettes_guarding_paris.png"], | |
} | |
], | |
[ | |
{ | |
"text": "What's unusual about this image?", | |
"files": [f"{examples_path}/example_images/dragons_playing.png"], | |
} | |
], | |
] | |
API_TOKEN = os.getenv("HF_AUTH_TOKEN") | |
HF_WRITE_TOKEN = os.getenv("HF_WRITE_TOKEN") | |
# IDEFICS_LOGO = "https://huggingface.co/spaces/HuggingFaceM4/idefics_playground/resolve/main/IDEFICS_logo.png" | |
BOT_AVATAR = "IDEFICS_logo.png" | |
# Chatbot utils | |
def turn_is_pure_media(turn): | |
return turn[1] is None | |
def load_image_from_url(url): | |
with urllib.request.urlopen(url) as response: | |
image_data = response.read() | |
image_stream = io.BytesIO(image_data) | |
image = Image.open(image_stream) | |
return image | |
def img_to_bytes(image_path): | |
image = Image.open(image_path).convert(mode='RGB') | |
buffer = io.BytesIO() | |
image.save(buffer, format="JPEG") | |
img_bytes = buffer.getvalue() | |
image.close() | |
return img_bytes | |
def format_user_prompt_with_im_history_and_system_conditioning( | |
user_prompt, chat_history | |
) -> List[Dict[str, Union[List, str]]]: | |
""" | |
Produces the resulting list that needs to go inside the processor. | |
It handles the potential image(s), the history and the system conditionning. | |
""" | |
resulting_messages = copy.deepcopy(SYSTEM_PROMPT) | |
resulting_images = [] | |
for resulting_message in resulting_messages: | |
if resulting_message["role"] == "user": | |
for content in resulting_message["content"]: | |
if content["type"] == "image": | |
resulting_images.append(load_image_from_url(content["image"])) | |
# Format history | |
for turn in chat_history: | |
if not resulting_messages or ( | |
resulting_messages and resulting_messages[-1]["role"] != "user" | |
): | |
resulting_messages.append( | |
{ | |
"role": "user", | |
"content": [], | |
} | |
) | |
if turn_is_pure_media(turn): | |
media = turn[0][0] | |
resulting_messages[-1]["content"].append({"type": "image"}) | |
resulting_images.append(Image.open(media)) | |
else: | |
user_utterance, assistant_utterance = turn | |
resulting_messages[-1]["content"].append( | |
{"type": "text", "text": user_utterance.strip()} | |
) | |
resulting_messages.append( | |
{ | |
"role": "assistant", | |
"content": [{"type": "text", "text": user_utterance.strip()}], | |
} | |
) | |
# Format current input | |
if not user_prompt["files"]: | |
resulting_messages.append( | |
{ | |
"role": "user", | |
"content": [{"type": "text", "text": user_prompt["text"]}], | |
} | |
) | |
else: | |
# Choosing to put the image first (i.e. before the text), but this is an arbiratrary choice. | |
resulting_messages.append( | |
{ | |
"role": "user", | |
"content": [{"type": "image"}] * len(user_prompt["files"]) | |
+ [{"type": "text", "text": user_prompt["text"]}], | |
} | |
) | |
resulting_images.extend([Image.open(path) for path in user_prompt["files"]]) | |
return resulting_messages, resulting_images | |
def extract_images_from_msg_list(msg_list): | |
all_images = [] | |
for msg in msg_list: | |
for c_ in msg["content"]: | |
if isinstance(c_, Image.Image): | |
all_images.append(c_) | |
return all_images | |
def model_inference( | |
user_prompt, | |
chat_history, | |
model_selector, | |
decoding_strategy, | |
temperature, | |
max_new_tokens, | |
repetition_penalty, | |
top_p, | |
): | |
if user_prompt["text"].strip() == "" and not user_prompt["files"]: | |
gr.Error("Please input a query and optionally image(s).") | |
if user_prompt["text"].strip() == "" and user_prompt["files"]: | |
gr.Error("Please input a text query along the image(s).") | |
streamer = TextIteratorStreamer( | |
PROCESSOR.tokenizer, | |
skip_prompt=True, | |
timeout=5.0, | |
) | |
# Common parameters to all decoding strategies | |
# This documentation is useful to read: https://huggingface.co/docs/transformers/main/en/generation_strategies | |
generation_args = { | |
"max_new_tokens": max_new_tokens, | |
"repetition_penalty": repetition_penalty, | |
"streamer": streamer, | |
} | |
assert decoding_strategy in [ | |
"Greedy", | |
"Top P Sampling", | |
] | |
if decoding_strategy == "Greedy": | |
generation_args["do_sample"] = False | |
elif decoding_strategy == "Top P Sampling": | |
generation_args["temperature"] = temperature | |
generation_args["do_sample"] = True | |
generation_args["top_p"] = top_p | |
# Creating model inputs | |
( | |
resulting_text, | |
resulting_images, | |
) = format_user_prompt_with_im_history_and_system_conditioning( | |
user_prompt=user_prompt, | |
chat_history=chat_history, | |
) | |
prompt = PROCESSOR.apply_chat_template(resulting_text, add_generation_prompt=True) | |
inputs = PROCESSOR( | |
text=prompt, | |
images=resulting_images if resulting_images else None, | |
return_tensors="pt", | |
) | |
inputs = {k: v.to(DEVICE) for k, v in inputs.items()} | |
generation_args.update(inputs) | |
# # The regular non streaming generation mode | |
# _ = generation_args.pop("streamer") | |
# generated_ids = MODELS[model_selector].generate(**generation_args) | |
# generated_text = PROCESSOR.batch_decode(generated_ids[:, generation_args["input_ids"].size(-1): ], skip_special_tokens=True)[0] | |
# return generated_text | |
# The streaming generation mode | |
thread = Thread( | |
target=MODELS[model_selector].generate, | |
kwargs=generation_args, | |
) | |
thread.start() | |
print("Start generating") | |
acc_text = "" | |
for text_token in streamer: | |
time.sleep(0.04) | |
acc_text += text_token | |
if acc_text.endswith("<end_of_utterance>"): | |
acc_text = acc_text[:-18] | |
yield acc_text | |
print("Success - generated the following text:", acc_text) | |
print("-----") | |
FEATURES = datasets.Features( | |
{ | |
"model_selector": datasets.Value("string"), | |
"images": datasets.Sequence(datasets.Image(decode=True)), | |
"conversation": datasets.Sequence({"User": datasets.Value("string"), "Assistant": datasets.Value("string")}), | |
"decoding_strategy": datasets.Value("string"), | |
"temperature": datasets.Value("float32"), | |
"max_new_tokens": datasets.Value("int32"), | |
"repetition_penalty": datasets.Value("float32"), | |
"top_p": datasets.Value("int32"), | |
} | |
) | |
def flag_dope( | |
model_selector, | |
chat_history, | |
decoding_strategy, | |
temperature, | |
max_new_tokens, | |
repetition_penalty, | |
top_p, | |
): | |
images = [] | |
conversation = [] | |
for ex in chat_history: | |
if isinstance(ex[0], dict): | |
images.append(img_to_bytes(ex[0]["file"]["path"])) | |
else: | |
conversation.append({"User": ex[0], "Assistant": ex[1]}) | |
data = { | |
"model_selector": [model_selector], | |
"images": [images], | |
"conversation": [conversation], | |
"decoding_strategy": [decoding_strategy], | |
"temperature": [temperature], | |
"max_new_tokens": [max_new_tokens], | |
"repetition_penalty": [repetition_penalty], | |
"top_p": [top_p], | |
} | |
try: | |
ds = datasets.load_dataset("HuggingFaceM4/dope-dataset-red-teaming", split="train", token=HF_WRITE_TOKEN) | |
new_data = datasets.Dataset.from_dict(data, features=FEATURES) | |
hf_dataset = datasets.concatenate_datasets([ds,new_data]) | |
except Exception: | |
hf_dataset = datasets.Dataset.from_dict(data, features=FEATURES) | |
hf_dataset.push_to_hub( "HuggingFaceM4/dope-dataset-red-teaming", split="train", token=HF_WRITE_TOKEN, private=True) | |
def flag_problematic( | |
model_selector, | |
chat_history, | |
decoding_strategy, | |
temperature, | |
max_new_tokens, | |
repetition_penalty, | |
top_p, | |
): | |
images = [] | |
conversation = [] | |
for ex in chat_history: | |
if isinstance(ex[0], dict): | |
images.append(img_to_bytes(ex[0]["file"]["path"])) | |
else: | |
conversation.append({"User": ex[0], "Assistant": ex[1]}) | |
data = { | |
"model_selector": [model_selector], | |
"images": [images], | |
"conversation": [conversation], | |
"decoding_strategy": [decoding_strategy], | |
"temperature": [temperature], | |
"max_new_tokens": [max_new_tokens], | |
"repetition_penalty": [repetition_penalty], | |
"top_p": [top_p], | |
} | |
try: | |
ds = datasets.load_dataset("HuggingFaceM4/problematic-dataset-red-teaming", split="train", token=HF_WRITE_TOKEN) | |
new_data = datasets.Dataset.from_dict(data, features=FEATURES) | |
hf_dataset = datasets.concatenate_datasets([ds,new_data]) | |
except Exception: | |
hf_dataset = datasets.Dataset.from_dict(data, features=FEATURES) | |
hf_dataset.push_to_hub( "HuggingFaceM4/problematic-dataset-red-teaming", split="train", token=HF_WRITE_TOKEN, private=True) | |
# Hyper-parameters for generation | |
max_new_tokens = gr.Slider( | |
minimum=8, | |
maximum=1024, | |
value=512, | |
step=1, | |
interactive=True, | |
label="Maximum number of new tokens to generate", | |
) | |
repetition_penalty = gr.Slider( | |
minimum=0.01, | |
maximum=5.0, | |
value=1.1, | |
step=0.01, | |
interactive=True, | |
label="Repetition penalty", | |
info="1.0 is equivalent to no penalty", | |
) | |
decoding_strategy = gr.Radio( | |
[ | |
"Greedy", | |
"Top P Sampling", | |
], | |
value="Greedy", | |
label="Decoding strategy", | |
interactive=True, | |
info="Higher values is equivalent to sampling more low-probability tokens.", | |
) | |
temperature = gr.Slider( | |
minimum=0.0, | |
maximum=5.0, | |
value=0.4, | |
step=0.1, | |
visible=False, | |
interactive=True, | |
label="Sampling temperature", | |
info="Higher values will produce more diverse outputs.", | |
) | |
top_p = gr.Slider( | |
minimum=0.01, | |
maximum=0.99, | |
value=0.8, | |
step=0.01, | |
visible=False, | |
interactive=True, | |
label="Top P", | |
info="Higher values is equivalent to sampling more low-probability tokens.", | |
) | |
chatbot = gr.Chatbot( | |
label="Idefics2-Chatty", | |
avatar_images=[None, BOT_AVATAR], | |
height=450, | |
) | |
# Using Flagging for saving dope and problematic examples | |
# Dope examples flagging | |
# gr.Markdown("""## How to use? | |
# There are two ways to provide image inputs: | |
# - Using the image box on the left panel | |
# - Using the inline syntax: `text<fake_token_around_image><image:URL_IMAGE><fake_token_around_image>text` | |
# The second syntax allows inputting an arbitrary number of images.""") | |
image_flag = gr.Image(visible=False) | |
with gr.Blocks( | |
fill_height=True, | |
css=""".gradio-container .avatar-container {height: 40px width: 40px !important;}""", | |
) as demo: | |
gr.Markdown("# πΆ Idefics2-Chatty Playground πΆ") | |
gr.Markdown("In this demo you'll be able to chat with [Idefics2-8B-chatty](https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty), a variant of [Idefics2-8B](https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty) further fine-tuned on chat datasets") | |
gr.Markdown("If you want to learn more about Idefics2 and its variants, you can check our [blog post](https://huggingface.co/blog/idefics2).") | |
# model selector should be set to `visbile=False` ultimately | |
with gr.Row(elem_id="model_selector_row"): | |
model_selector = gr.Dropdown( | |
choices=MODELS.keys(), | |
value=list(MODELS.keys())[0], | |
interactive=True, | |
show_label=False, | |
container=False, | |
label="Model", | |
visible=False, | |
) | |
decoding_strategy.change( | |
fn=lambda selection: gr.Slider( | |
visible=( | |
selection | |
in [ | |
"contrastive_sampling", | |
"beam_sampling", | |
"Top P Sampling", | |
"sampling_top_k", | |
] | |
) | |
), | |
inputs=decoding_strategy, | |
outputs=temperature, | |
) | |
decoding_strategy.change( | |
fn=lambda selection: gr.Slider( | |
visible=( | |
selection | |
in [ | |
"contrastive_sampling", | |
"beam_sampling", | |
"Top P Sampling", | |
"sampling_top_k", | |
] | |
) | |
), | |
inputs=decoding_strategy, | |
outputs=repetition_penalty, | |
) | |
decoding_strategy.change( | |
fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])), | |
inputs=decoding_strategy, | |
outputs=top_p, | |
) | |
gr.ChatInterface( | |
fn=model_inference, | |
chatbot=chatbot, | |
examples=EXAMPLES, | |
multimodal=True, | |
cache_examples=False, | |
additional_inputs=[ | |
model_selector, | |
decoding_strategy, | |
temperature, | |
max_new_tokens, | |
repetition_penalty, | |
top_p, | |
], | |
) | |
with gr.Group(): | |
with gr.Row(): | |
with gr.Column(scale=1, min_width=50): | |
dope_bttn = gr.Button("Dopeπ₯") | |
with gr.Column(scale=1, min_width=50): | |
problematic_bttn = gr.Button("Problematicπ¬") | |
dope_bttn.click( | |
fn=flag_dope, | |
inputs=[ | |
model_selector, | |
chatbot, | |
decoding_strategy, | |
temperature, | |
max_new_tokens, | |
repetition_penalty, | |
top_p, | |
], | |
outputs=None, | |
preprocess=False, | |
) | |
problematic_bttn.click( | |
fn=flag_problematic, | |
inputs=[ | |
model_selector, | |
chatbot, | |
decoding_strategy, | |
temperature, | |
max_new_tokens, | |
repetition_penalty, | |
top_p, | |
], | |
outputs=None, | |
preprocess=False, | |
) | |
demo.launch() | |