import gradio as gr from transformers import AutoProcessor, AutoModelForVision2Seq import re import time from PIL import Image import torch import spaces import subprocess #subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM-Instruct") processor.chat_template = """<|begin_of_text|>{% for message in messages %}{{message['role'].capitalize()}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '' }}{% endif %}{% endfor %}\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}""" model = AutoModelForVision2Seq.from_pretrained("HuggingFaceTB/SmolVLM-Instruct", torch_dtype=torch.bfloat16, #_attn_implementation="flash_attention_2" ).to("cuda") @spaces.GPU def model_inference( images, text, assistant_prefix, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p ): if text == "" and not images: gr.Error("Please input a query and optionally image(s).") if text == "" and images: gr.Error("Please input a text query along the image(s).") if isinstance(images, Image.Image): images = [images] resulting_messages = [ { "role": "user", "content": [{"type": "image"}] + [ {"type": "text", "text": text} ] } ] if assistant_prefix: text = f"{assistant_prefix} {text}" prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True) inputs = processor(text=prompt, images=[images], return_tensors="pt") inputs = {k: v.to("cuda") for k, v in inputs.items()} generation_args = { "max_new_tokens": max_new_tokens, "repetition_penalty": repetition_penalty, } 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 generation_args.update(inputs) # Generate generated_ids = model.generate(**generation_args) generated_texts = processor.batch_decode(generated_ids[:, generation_args["input_ids"].size(1):], skip_special_tokens=True) return generated_texts[0] with gr.Blocks(fill_height=False) as demo: gr.Markdown("## SmolVLM: Small yet Mighty 💫") gr.Markdown("Play with [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct) in this demo. To get started, upload an image and text or try one of the examples.") with gr.Column(): with gr.Row(): image_input = gr.Image(label="Upload your Image", type="pil") with gr.Column(): query_input = gr.Textbox(label="Prompt") assistant_prefix = gr.Textbox(label="Assistant Prefix", placeholder="Let's think step by step.") submit_btn = gr.Button("Submit") output = gr.Textbox(label="Output") with gr.Accordion(label="Advanced Generation Parameters", open=False): examples=[ ["example_images/rococo.jpg", "What art era is this?", "", "Greedy", 0.4, 512, 1.2, 0.8], ["example_images/examples_wat_arun.jpg", "Give me travel tips for the area around this monument.", "", "Greedy", 0.4, 512, 1.2, 0.8], ["example_images/examples_invoice.png", "What is the due date and the invoice date?", "", "Greedy", 0.4, 512, 1.2, 0.8], ["example_images/s2w_example.png", "What is this UI about?", "", "Greedy", 0.4, 512, 1.2, 0.8], ["example_images/examples_weather_events.png", "Where do the severe droughts happen according to this diagram?", "", "Greedy", 0.4, 512, 1.2, 0.8], ] # 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=1.2, step=0.01, interactive=True, label="Repetition penalty", info="1.0 is equivalent to no penalty", ) temperature = gr.Slider( minimum=0.0, maximum=5.0, value=0.4, step=0.1, 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, interactive=True, label="Top P", info="Higher values is equivalent to sampling more low-probability tokens.", ) 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.", ) 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.Examples( examples = examples, inputs=[image_input, query_input, assistant_prefix, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p], outputs=output, fn=model_inference ) submit_btn.click(model_inference, inputs = [image_input, query_input, assistant_prefix, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p], outputs=output) demo.launch(debug=True)