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 User’s 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.", }, ], } ] 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) 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 @spaces.GPU(duration=180) 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(""): acc_text = acc_text[:-18] yield acc_text print("Success - generated the following text:", acc_text) print("-----") def flag_dope( model_selector, chat_history, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p, ): images = [] for ex in chat_history: if isinstance(ex[0], dict): images.append(ex[0]["file"]) prev_ex_is_image = True if len(images)== 0: black_image = Image.new('RGB', (20, 20), (0, 0, 0)) black_image.save("/tmp/gradio/fake_image.png") image_flag = {'path': "/tmp/gradio/fake_image.png", 'size': None, 'orig_name': None, 'mime_type': 'image/png', 'is_stream': False, 'meta': {'_type': 'gradio.FileData'}} else: image_flag = images[0] dope_dataset_writer.flag( flag_data=[ model_selector, image_flag, chat_history, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p, ] ) def flag_problematic( model_selector, chat_history, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p, ): images = [] for ex in chat_history: if isinstance(ex[0], dict): images.append(ex[0]["file"]) if len(images)== 0: black_image = Image.new('RGB', (20, 20), (0, 0, 0)) black_image.save("/tmp/gradio/fake_image.png") image_flag = {'path': "/tmp/gradio/fake_image.png", 'size': None, 'orig_name': None, 'mime_type': 'image/png', 'is_stream': False, 'meta': {'_type': 'gradio.FileData'}} else: image_flag = images[0] problematic_dataset_writer.flag( flag_data=[ model_selector, image_flag, chat_history, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p, ] ) # 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", avatar_images=[None, BOT_AVATAR], height=450, ) dope_dataset_writer = gr.HuggingFaceDatasetSaver( HF_WRITE_TOKEN, "HuggingFaceM4/dope-dataset", private=True ) problematic_dataset_writer = gr.HuggingFaceDatasetSaver( HF_WRITE_TOKEN, "HuggingFaceM4/problematic-dataset", private=True ) # 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: `texttext` # 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: # 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=True, ) 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=[{"text": "hello"}, {"text": "hola"}, {"text": "merhaba"}], title="Idefics2 Playground", multimodal=True, 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("Dope🔥") with gr.Column(scale=1, min_width=50): problematic_bttn = gr.Button("Problematic😬") dope_dataset_writer.setup( [ model_selector, image_flag, chatbot, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p, ], "gradio_dope_data_points", ) 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 examples flagging problematic_dataset_writer.setup( [ model_selector, image_flag, chatbot, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p, ], "gradio_problematic_data_points", ) 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()