app.py

import gradio as gr
from transformers import AutoProcessor, AutoModelForVision2Seq, TextIteratorStreamer
from transformers.image_utils import load_image
from threading import Thread
import re
import time
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-250M")
model = AutoModelForVision2Seq.from_pretrained("HuggingFaceTB/SmolVLM-Instruct-250M", 
        torch_dtype=torch.bfloat16,
        #_attn_implementation="flash_attention_2"
        ).to("cuda")

@spaces.GPU
def model_inference(
    input_dict, history
): 
    text = input_dict["text"]
    print(input_dict["files"])
    if len(input_dict["files"]) > 1:
      images = [load_image(image) for image in input_dict["files"]]
    elif len(input_dict["files"]) == 1:
      images = [load_image(input_dict["files"][0])]   
    else:
      images = []
    

    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).")

    


    resulting_messages = [
                {
                    "role": "user",
                    "content": [{"type": "image"} for _ in range(len(images))] + [
                        {"type": "text", "text": text}
                    ]
                }
            ]
    prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True)
    inputs = processor(text=prompt, images=[images], return_tensors="pt")
    inputs = inputs.to('cuda')
    generation_args = {
        "input_ids": inputs.input_ids,
        "pixel_values": inputs.pixel_values,
        "attention_mask": inputs.attention_mask,
        "num_return_sequences": 1,
        "no_repeat_ngram_size": 2,
        "max_new_tokens": 500,
        "min_new_tokens": 10,       
    }

    # Generate
    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
    generation_args = dict(inputs, streamer=streamer, max_new_tokens=500)
    generated_text = ""

    thread = Thread(target=model.generate, kwargs=generation_args)
    thread.start()

    yield "..."
    buffer = ""
    
      
    for new_text in streamer:
    
      buffer += new_text
      generated_text_without_prompt = buffer#[len(ext_buffer):]
      time.sleep(0.01)
      yield buffer


examples=[
              [{"text": "Describe this image.", "files": ["example_images/newyork.jpg"]}],
              [{"text": "Describe this image.", "files": ["example_images/dogs.jpg"]}],
              [{"text": "Where do the severe droughts happen according to this diagram?", "files": ["example_images/examples_weather_events.png"]}],
              [{"text": "What art era do these artpieces belong to?", "files": ["example_images/rococo.jpg", "example_images/rococo_1.jpg"]}],
              [{"text": "Describe this image.", "files": ["example_images/campeones.jpg"]}],
              [{"text": "What does this say?", "files": ["example_images/math.jpg"]}],
              [{"text": "What is the date in this document?", "files": ["example_images/document.jpg"]}],
              [{"text": "What is this UI about?", "files": ["example_images/s2w_example.png"]}],
      ]
demo = gr.ChatInterface(fn=model_inference, title="SmolVLM-250M: The Smollest VLM ever 💫", 
                description="Play with [HuggingFaceTB/SmolVLM-Instruct-250M](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct-250M) in this demo. To get started, upload an image and text or try one of the examples. This demo doesn't use history for the chat, so every chat you start is a new conversation.",
                examples=examples,
                textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True,
                cache_examples=False
                )
      
      

demo.launch(debug=True)