app.py

import gradio as gr
from transformers import AutoProcessor, AutoModelForImageTextToText, TextIteratorStreamer
from threading import Thread
import re
import time
import torch
import spaces
import subprocess

# Ensure pyav is installed
subprocess.run('pip install pyav', shell=True, check=True)
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

from io import BytesIO

processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct", 
                                               _attn_implementation="flash_attention_2",
                                               torch_dtype=torch.bfloat16).to("cuda:0")

@spaces.GPU
def model_inference(
    input_dict, history, max_tokens
): 
    text = input_dict["text"]
    images = []
    user_content = []
    media_queue = []
    if history == []:
        text = input_dict["text"].strip() 
        
        for file in input_dict.get("files", []):
            if file.endswith((".png", ".jpg", ".jpeg", ".gif", ".bmp")):
                media_queue.append({"type": "image", "path": file})
            elif file.endswith((".mp4", ".mov", ".avi", ".mkv", ".flv")):
                media_queue.append({"type": "video", "path": file})

        if "<image>" in text or "<video>" in text:
            parts = re.split(r'(<image>|<video>)', text)  
            for part in parts:
                if part == "<image>" and media_queue:
                    user_content.append(media_queue.pop(0)) 
                elif part == "<video>" and media_queue:
                    user_content.append(media_queue.pop(0))  
                elif part.strip():  
                    user_content.append({"type": "text", "text": part.strip()})
        else:
            user_content.append({"type": "text", "text": text})
            
            for media in media_queue:
                user_content.append(media)

        resulting_messages = [{"role": "user", "content": user_content}]

    elif len(history) > 0:
        resulting_messages = []
        user_content = []
        media_queue = []
        for hist in history:
            if hist["role"] == "user" and isinstance(hist["content"], tuple): 
                file_name = hist["content"][0]
            if file_name.endswith((".png", ".jpg", ".jpeg")):
                media_queue.append({"type": "image", "path": file_name})
            elif file_name.endswith(".mp4"):
                media_queue.append({"type": "video", "path": file_name})

        for hist in history:
            if hist["role"] == "user" and isinstance(hist["content"], str): 
                text = hist["content"]
                parts = re.split(r'(<image>|<video>)', text)  
                
                for part in parts:
                    if part == "<image>" and media_queue:
                        user_content.append(media_queue.pop(0)) 
                    elif part == "<video>" and media_queue:
                        user_content.append(media_queue.pop(0))  
                    elif part.strip(): 
                        user_content.append({"type": "text", "text": part.strip()})
            
            elif hist["role"] == "assistant":
                resulting_messages.append({
                    "role": "user",
                    "content": user_content
                })
                resulting_messages.append({
                    "role": "assistant",
                    "content": [{"type": "text", "text": hist["content"]}]
                })
                user_content = [] 

    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 images(s).")
    print("resulting_messages", resulting_messages)
    inputs = processor.apply_chat_template(
        resulting_messages,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt",
    )

    inputs = inputs.to(model.device)
    
    # Generate
    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
    generation_args = dict(inputs, streamer=streamer, max_new_tokens=max_tokens)
    generated_text = ""

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

    yield "..."
    buffer = ""
    
    for new_text in streamer:
        buffer += new_text
        time.sleep(0.01)
        yield buffer

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. This demo doesn't use history for the chat, so every chat you start is a new conversation.",
                textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image", ".mp4"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True,
                cache_examples=False,
                additional_inputs=[gr.Slider(minimum=100, maximum=500, step=50, value=200, label="Max Tokens")],
                type="messages"
                )

demo.launch(debug=True, share=True)