app.py

# Imports
import gradio as gr
import spaces
import torch
import os
import math
import tempfile
import librosa
from PIL import Image, ImageSequence
from decord import VideoReader, cpu
from moviepy.editor import VideoFileClip
from transformers import AutoModel, AutoTokenizer, AutoProcessor

# Variables
DEVICE = "auto"
if DEVICE == "auto":
    DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"[SYSTEM] | Using {DEVICE} type compute device.")

DEFAULT_INPUT = "Describe in one short sentence."
MAX_FRAMES = 64

model_name = "openbmb/MiniCPM-o-2_6"

repo = AutoModel.from_pretrained(model_name, trust_remote_code=True, attn_implementation="sdpa", torch_dtype=torch.bfloat16).to(DEVICE)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)

css = '''
.gradio-container{max-width: 560px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''

input_prefixes = {
    "Image": "(A image file called █ has been attached, describe the image content) ",
    "GIF": "(A GIF file called █ has been attached, describe the GIF content) ",
    "Video": "(A video with audio file called █ has been attached, describe the video content and the audio content embedded into the video) ",
    "Audio": "(A audio file called █ has been attached, describe the audio content) ",
}

filetypes = {
    "Image": [".jpg", ".jpeg", ".png", ".bmp"],
    "GIF": [".gif"],
    "Video": [".mp4", ".mov", ".avi", ".mkv"],
    "Audio": [".wav", ".mp3", ".flac", ".aac"],
}

def uniform_sample(idxs, n):
    gap = len(idxs) / n
    return [idxs[int(i * gap + gap / 2)] for i in range(n)]

def build_omni_chunks(path, prefix, instruction, sr=AUDIO_SR, seconds_per_unit=1):
    clip = VideoFileClip(path, audio_fps=sr)
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
        clip.audio.write_audiofile(tmp.name, fps=sr, codec="pcm_s16le", verbose=False, logger=None)
        audio_np, _ = librosa.load(tmp.name, sr=sr, mono=True)
    total_units = math.ceil(clip.duration / seconds_per_unit)
    content = []
    for i in range(total_units):
        t = min(i * seconds_per_unit, clip.duration - 1e-3)
        frame = Image.fromarray(clip.get_frame(t).astype("uint8")).convert("RGB")
        audio_chunk = audio_np[sr * i * seconds_per_unit : sr * (i + 1) * seconds_per_unit]
        content.extend(["<unit>", frame, audio_chunk])
    clip.close()
    os.remove(tmp.name)
    content.append(prefix + instruction)
    return content

def build_image_omni(path, prefix, instruction):
    image = Image.open(path).convert("RGB")
    return ["<unit>", image, prefix + instruction]

def encode_gif(path):
    img = Image.open(path)
    frames = [frame.copy().convert("RGB") for frame in ImageSequence.Iterator(img)]
    if len(frames) > MAX_FRAMES:
        frames = uniform_sample(frames, MAX_FRAMES)
    return frames

def build_gif_omni(path, prefix, instruction):
    frames = encode_gif(path)
    content = []
    for f in frames:
        content.extend(["<unit>", f])
    content.append(prefix + instruction)
    return content

def build_audio_omni(path, prefix, instruction, sr=AUDIO_SR):
    audio_np, _ = librosa.load(path, sr=sr, mono=True)
    return ["<unit>", audio_np, prefix + instruction]

@spaces.GPU(duration=60)
def generate(input, instruction=DEFAULT_INPUT, sampling=False, temperature=0.7, top_p=0.8, top_k=100, repetition_penalty=1.05, max_tokens=512):
    if not input: return "No input provided."
    
    extension = os.path.splitext(input)[1].lower()
    filetype = next((k for k, v in filetypes.items() if extension in v), None)
    
    if not filetype: return "Unsupported file type."
    
    filename = os.path.basename(input)
    prefix = input_prefixes[filetype].replace("█", filename)
    if filetype == "Video":
        omni_content = build_omni_chunks(input, prefix, instruction)
    elif filetype == "Image":
        omni_content = build_image_omni(input, prefix, instruction)
    elif filetype == "GIF":
        omni_content = build_gif_omni(input, prefix, instruction)
    elif filetype == "Audio":
        omni_content = build_audio_omni(input, prefix, instruction)
    
    sys_msg = repo.get_sys_prompt(mode="omni", language="en")
    msgs = [sys_msg, {"role": "user", "content": omni_content}]
    
    params = {
        "msgs": msgs,
        "tokenizer": tokenizer,
        "sampling": sampling,
        "temperature": temperature,
        "top_p": top_p,
        "top_k": top_k,
        "repetition_penalty": repetition_penalty,
        "max_new_tokens": max_tokens,
        "omni_input": True,
    }
    
    output = repo.chat(**params)
    torch.cuda.empty_cache()
    gc.collect()
    return output

def cloud():
    print("[CLOUD] | Space maintained.")

# Initialize
with gr.Blocks(css=css) as main:
    with gr.Column():
        input = gr.File(label="Input", file_types=["image", "video", "audio"], type="filepath")
        instruction = gr.Textbox(lines=1, value=DEFAULT_INPUT, label="Instruction")
        sampling = gr.Checkbox(value=False, label="Sampling")
        temperature = gr.Slider(minimum=0.01, maximum=1.99, step=0.01, value=0.7, label="Temperature")
        top_p = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.8, label="Top P")
        top_k = gr.Slider(minimum=0, maximum=1000, step=1, value=100, label="Top K")
        repetition_penalty = gr.Slider(minimum=0.01, maximum=1.99, step=0.01, value=1.05, label="Repetition Penalty")
        max_tokens = gr.Slider(minimum=1, maximum=4096, step=1, value=512, label="Max Tokens")
        submit = gr.Button("▶")
        maintain = gr.Button("☁️")

    with gr.Column():
        output = gr.Textbox(lines=1, value="", label="Output")

    submit.click(fn=generate, inputs=[input, instruction, sampling, temperature, top_p, top_k, repetition_penalty, max_tokens], outputs=[output], queue=False)
    maintain.click(cloud, inputs=[], outputs=[], queue=False)

main.launch(show_api=True)