app.py

# Imports
import gradio as gr
import spaces
import torch

from PIL import Image
from transformers import AutoModel, AutoTokenizer

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

# Variables
DEFAULT_INPUT = "Describe in one paragraph."

repo = AutoModel.from_pretrained("openbmb/MiniCPM-V-2_6", torch_dtype=torch.bfloat16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM-V-2_6", trust_remote_code=True)

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

# Functions
@spaces.GPU(duration=60)
def generate(image, instruction=DEFAULT_INPUT, sampling=False, temperature=0.7, top_p=0.8, top_k=100, repetition_penalty=1.05, max_tokens=512):
    global model, tokenizer
    
    image_rgb = Image.open(image).convert("RGB")
    print(image_rgb, instruction)

    inputs = [{"role": "user", "content": [image_rgb, instruction]}]

    parameters = {
        "sampling": sampling,
        "temperature": temperature,
        "top_p": top_p,
        "top_k": top_k,
        "repetition_penalty": repetition_penalty,
        "max_new_tokens": max_tokens
    }
    
    output = model.chat(image=None, msgs=inputs, tokenizer=tokenizer, **parameters)
    
    return output
    
def cloud():
    print("[CLOUD] | Space maintained.")

# Initialize
with gr.Blocks(css=css) as main:
    with gr.Column():
        gr.Markdown("🪄 Analyze images and caption them using state-of-the-art openbmb/MiniCPM-V-2_6.")
        
    with gr.Column():
        input = gr.Image(label="Image")
        instruction = gr.Textbox(lines=1, value=DEFAULT_INPUT, label="Instruction")
        sampling = gr.Checkbox(value=False, label="Sampling")
        temperature = gr.Slider(minimum=0, maximum=2, 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, maximum=2, 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)