app.py

import gradio as gr
import torch
import re
from decord import VideoReader, cpu
from PIL import Image
import numpy as np
import transformers
from typing import Dict, Optional, Sequence, List

import sys
from oryx.conversation import conv_templates, SeparatorStyle
from oryx.model.builder import load_pretrained_model
from oryx.utils import disable_torch_init
from oryx.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria, process_anyres_video_genli
from oryx.constants import IGNORE_INDEX, DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX


model_path = "THUdyh/Oryx-7B"
model_name = get_model_name_from_path(model_path)
overwrite_config = {}
overwrite_config["mm_resampler_type"] = "dynamic_compressor"
overwrite_config["patchify_video_feature"] = False
overwrite_config["attn_implementation"] = "sdpa" if torch.__version__ >= "2.1.2" else "eager"
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, device_map="cuda:0", overwrite_config=overwrite_config)
model.to('cuda').eval()

def preprocess_qwen(sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict:
    roles = {"human": "<|im_start|>user", "gpt": "<|im_start|>assistant"}

    im_start, im_end = tokenizer.additional_special_tokens_ids
    nl_tokens = tokenizer("\n").input_ids
    _system = tokenizer("system").input_ids + nl_tokens
    _user = tokenizer("user").input_ids + nl_tokens
    _assistant = tokenizer("assistant").input_ids + nl_tokens

    # Apply prompt templates
    input_ids, targets = [], []

    source = sources
    if roles[source[0]["from"]] != roles["human"]:
        source = source[1:]

    input_id, target = [], []
    system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens
    input_id += system
    target += [im_start] + [IGNORE_INDEX] * (len(system) - 3) + [im_end] + nl_tokens
    assert len(input_id) == len(target)
    for j, sentence in enumerate(source):
        role = roles[sentence["from"]]
        if has_image and sentence["value"] is not None and "<image>" in sentence["value"]:
            num_image = len(re.findall(DEFAULT_IMAGE_TOKEN, sentence["value"]))
            texts = sentence["value"].split('<image>')
            _input_id = tokenizer(role).input_ids + nl_tokens 
            for i,text in enumerate(texts):
                _input_id += tokenizer(text).input_ids 
                if i<len(texts)-1:
                    _input_id += [IMAGE_TOKEN_INDEX] + nl_tokens
            _input_id += [im_end] + nl_tokens
            assert sum([i==IMAGE_TOKEN_INDEX for i in _input_id])==num_image
        else:
            if sentence["value"] is None:
                _input_id = tokenizer(role).input_ids + nl_tokens
            else:
                _input_id = tokenizer(role).input_ids + nl_tokens + tokenizer(sentence["value"]).input_ids + [im_end] + nl_tokens
        input_id += _input_id
        if role == "<|im_start|>user":
            _target = [im_start] + [IGNORE_INDEX] * (len(_input_id) - 3) + [im_end] + nl_tokens
        elif role == "<|im_start|>assistant":
            _target = [im_start] + [IGNORE_INDEX] * len(tokenizer(role).input_ids) + _input_id[len(tokenizer(role).input_ids) + 1 : -2] + [im_end] + nl_tokens
        else:
            raise NotImplementedError
        target += _target

    input_ids.append(input_id)
    targets.append(target)
    input_ids = torch.tensor(input_ids, dtype=torch.long)
    targets = torch.tensor(targets, dtype=torch.long)
    return input_ids


def oryx_inference(video, text):
    vr = VideoReader(video, ctx=cpu(0))
    total_frame_num = len(vr)
    fps = round(vr.get_avg_fps())
    uniform_sampled_frames = np.linspace(0, total_frame_num - 1, 64, dtype=int)
    frame_idx = uniform_sampled_frames.tolist()
    spare_frames = vr.get_batch(frame_idx).asnumpy()
    video = [Image.fromarray(frame) for frame in spare_frames]

    conv_mode = "qwen_1_5"
    
    question = text
    question = "<image>\n" + question

    conv = conv_templates[conv_mode].copy()
    conv.append_message(conv.roles[0], question)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()

    input_ids = preprocess_qwen([{'from': 'human','value': question},{'from': 'gpt','value': None}], tokenizer, has_image=True).cuda()

    video_processed = []
    for idx, frame in enumerate(video):
        image_processor.do_resize = False
        image_processor.do_center_crop = False
        frame = process_anyres_video_genli(frame, image_processor)

        if frame_idx is not None and idx in frame_idx:
            video_processed.append(frame.unsqueeze(0))
        elif frame_idx is None:
            video_processed.append(frame.unsqueeze(0))
    
    if frame_idx is None:
        frame_idx = np.arange(0, len(video_processed), dtype=int).tolist()
    
    video_processed = torch.cat(video_processed, dim=0).bfloat16().cuda()
    video_processed = (video_processed, video_processed)

    video_data = (video_processed, (384, 384), "video")

    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
    keywords = [stop_str]

    with torch.inference_mode():
        output_ids = model.generate(
            inputs=input_ids,
            images=video_data[0][0],
            images_highres=video_data[0][1],
            modalities=video_data[2],
            do_sample=False,
            temperature=0,
            max_new_tokens=1024,
            use_cache=True,
        )

    
    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
    outputs = outputs.strip()
    if outputs.endswith(stop_str):
        outputs = outputs[:-len(stop_str)]
    outputs = outputs.strip()
    return outputs

# Define input and output for the Gradio interface
demo = gr.Interface(
    fn=oryx_inference,
    inputs=[gr.Video(label="Input Video"), gr.Textbox(label="Input Text")],
    outputs="text",
    title="Oryx Inference",
    description="This is a demo for Oryx inference."
)

# Launch the Gradio app
demo.launch(server_name="0.0.0.0",server_port=80)