import gradio as gr from transformers import AutoProcessor, AutoModelForVision2Seq, TextIteratorStreamer from threading import Thread 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) import requests import torch from PIL import Image from transformers import AutoModelForCausalLM, AutoProcessor from decord import VideoReader from decord import cpu from PIL import Image import numpy as np def load_video(video_path, frames=32): """ Load a video and extract a specified number of frames as PIL.Image objects. Parameters: - video_path (str): Path to the video file. - frames (int): Number of frames to extract. Returns: - List[PIL.Image]: A list of PIL.Image objects for the extracted frames. """ # Initialize VideoReader vr = VideoReader(video_path, ctx=cpu()) total_frames = len(vr) # Select frame indices evenly spaced throughout the video frame_indices = np.linspace(0, total_frames - 1, frames, dtype=int) # Extract frames and convert to PIL.Images images = [] for idx in frame_indices: frame = vr[idx] # Get the frame as a NumPy array image = Image.fromarray(frame.asnumpy()) # Convert to PIL.Image images.append(image) return images model_id_or_path = "teowu/Aria-Chat-Preview" model = AutoModelForCausalLM.from_pretrained(model_id_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True) @spaces.GPU def model_inference( input_dict, history, decoding_strategy, temperature, max_new_tokens, top_p ): text = input_dict["text"] print(input_dict["files"]) if len(input_dict["files"]) > 1: images = [Image.open(image).convert("RGB") for image in input_dict["files"]] elif len(input_dict["files"]) == 1: if input_dict["files"][0].endswith(".mp4") or input_dict["files"][0].endswith(".avi"): images = load_video(input_dict["files"][0]) else: images = [Image.open(input_dict["files"][0]).convert("RGB")] else: images = [] if text == "" and not images: gr.Error("Please input a query and optionally image(s).") if text == "" and images: text = "Please provide a detailed description." #gr.Error("Please input a text query along the image(s).") resulting_messages = [ { "role": "user", "content": [{"type": "image", "text": None} for _ in range(len(images))] + [ {"type": "text", "text": "\n" + 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 streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens= True) generation_args = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens) 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": "What art era do these artpieces belong to?", "files": ["example_images/rococo.jpg", "example_images/rococo_1.jpg"]}, "Greedy", 0.4, 512, 1.2, 0.8], [{"text": "I'm planning a visit to this temple, give me travel tips.", "files": ["example_images/examples_wat_arun.jpg"]}, "Greedy", 0.4, 512, 1.2, 0.8], [{"text": "What is the due date and the invoice date?", "files": ["example_images/examples_invoice.png"]}, "Greedy", 0.4, 512, 1.2, 0.8], [{"text": "What is this UI about?", "files": ["example_images/s2w_example.png"]}, "Greedy", 0.4, 512, 1.2, 0.8], [{"text": "Where do the severe droughts happen according to this diagram?", "files": ["example_images/examples_weather_events.png"]}, "Greedy", 0.4, 512, 1.2, 0.8], ] demo = gr.ChatInterface(fn=model_inference, title="Aria-Chat: Improved Real-world Abilties for Open-source LMMs on Images and Videos", description="Play with [rhymes-ai/Aria-Chat-Preview](https://huggingface.co/rhymes-ai/Aria-Chat-Preview) in this demo. To get started, upload an image (or a video) and text or try one of the examples. This checkpoint works best with single turn conversations, so clear the conversation after a single turn.", examples=examples, textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True, additional_inputs=[gr.Radio(["Top P Sampling", "Greedy"], value="Greedy", label="Decoding strategy", #interactive=True, info="Higher values is equivalent to sampling more low-probability tokens.", ), 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.", ), gr.Slider( minimum=8, maximum=1024, value=512, step=1, interactive=True, label="Maximum number of new tokens to generate", ), 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.", )],cache_examples=False ) demo.launch(debug=True)