app.py

import gradio as gr
from transformers import AutoProcessor, AutoModelForVision2Seq, TextIteratorStreamer
from transformers.models.smolvlm.video_processing_smolvlm import load_smolvlm_video
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)

from io import BytesIO
from transformers.image_utils import load_image

processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct")
model = AutoModelForVision2Seq.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct", 
                                               _attn_implementation="flash_attention_2",
                                               torch_dtype=torch.bfloat16, device_map="auto")


#@spaces.GPU
def model_inference(
    input_dict, history
): 
    text = input_dict["text"]
    # first turn input_dict {'text': 'What', 'files': ['/tmp/gradio/0350274350a64a5737e1a5732f014aee2f28bb7344bbad5105c0d0b7e7334375/cats_2.mp4', '/tmp/gradio/2dd39f382fcf5444a1a2ac57ed6f9acafa775dd855248cf273034e8ce18aeff4/IMG_2201.JPG']}
    # first turn  history []
    print("input_dict", input_dict)
    print("history", history)
    print("model.device", model.device)
    images = []
    # first conv turn
    if history == []:
        text = input_dict["text"]
        resulting_messages = [{"role": "user", "content": [{"type": "text"}, {"type": "text", "text": text}]}]
        for file in input_dict["files"]:
            if file.endswith(".mp4"):
                resulting_messages[0]["content"].append({"type": "video"})
                frames, timestamps, duration_sec = load_smolvlm_video(
                    file, sampling_fps=1, max_frames=64
                )
                print("frames", frames)
                images.append(frames)
            elif file.endswith(".jpg") or file.endswith(".jpeg") or file.endswith(".png"):
                resulting_messages[0]["content"].append({"type": "image"})
                images.append(load_image(file))
                print("images", images)

    # second turn input_dict {'text': 'what', 'files': ['/tmp/gradio/7bafdcc4722c4b9902a4936439b3bb694927abd72106a946d773a15cc1c630d7/IMG_2198.JPG']}
    # second turn history [[('/tmp/gradio/7bafdcc4722c4b9902a4936439b3bb694927abd72106a946d773a15cc1c630d7/IMG_2198.JPG',), None], 
    # [('/tmp/gradio/5b105e97e4876912b4e763902144540bd3ab00d9fd4016491337ee4f4c36f320/football.mp4',), None], ['what', None]]
    
    # later conv turn
    elif len(history) > 0:
        for hist in history:
           if isinstance(hist[0], tuple):
              if hist[0][0].endswith(".mp4"):
                 resulting_messages.append({"role": "user", "content": [{"type": "video"}, {"type": "text", "text": hist[0][0]}]})
                 frames, timestamps, duration_sec = load_smolvlm_video(
                    file, sampling_fps=1, max_frames=64
                )
                 images.append(frames)
              else:
                 resulting_messages.append({"role": "user", "content": [{"type": "image"}, {"type": "text", "text": hist[0][0]}]})
                 images.append(load_image(hist[0][0]))
           elif isinstance(hist[0], str):
              resulting_messages.append({"role": "user", "content": [{"type": "text"}, {"type": "text", "text": hist[0]}]})
           if isinstance(hist[1], str):
              resulting_messages.append({"role": "user", "content": [{"type": "text"}, {"type": "text", "text": hist[0]}]})
    


    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).")

    print("resulting_messages", resulting_messages)
    prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True)

    inputs = processor(text=prompt, images=[images], padding=True, return_tensors="pt")
    inputs = inputs.to(model.device)
    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": "Can you describe this image?", "files": ["example_images/newyork.jpg"]}],
              [{"text": "Can you 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="SmolVLM2: The Smollest Video Model Ever 📺", 
                description="Play with [SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) 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", ".mp4"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True,
                cache_examples=False,
                type="messages"
                )
      
      

demo.launch(debug=True)