app.py

import gradio as gr
import torch
import numpy as np
import torch.nn.functional as F
import PIL
from threading import Thread
from transformers import AutoModel, AutoProcessor
from transformers import StoppingCriteria, TextIteratorStreamer, StoppingCriteriaList
from torchvision.transforms.functional import normalize
from huggingface_hub import hf_hub_download
from briarmbg import BriaRMBG
from PIL import Image
from typing import Tuple


net=BriaRMBG()
# model_path = "./model1.pth"
model_path = hf_hub_download("briaai/RMBG-1.4", 'model.pth')
if torch.cuda.is_available():
    net.load_state_dict(torch.load(model_path))
    net=net.cuda()
else:
    net.load_state_dict(torch.load(model_path,map_location="cpu"))
net.eval() 


device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = AutoModel.from_pretrained("unum-cloud/uform-gen2-qwen-500m", trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-qwen-500m", trust_remote_code=True)

class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [151645]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False

def getProductDetails(history, image):
    product_description=getImageDescription(image)
    clients = InferenceClient("google/gemma-7b")
    rand_val = random.randint(1, 1111111111111111)
    if not history:
        history = []
    generate_kwargs = dict(
        temperature=temp,
        max_new_tokens=tokens,
        top_p=top_p,
        repetition_penalty=rep_p,
        do_sample=True,
        seed=seed,
    )
    system_prompt="you're a helpful e-commerce marketting assitant"
    prompt="Write me a poem"
    formatted_prompt = self.format_prompt(f"{system_prompt}, {prompt}", history)
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=stream_output, details=True, return_full_text=False)
    output = ""
    
    for response in stream:
        output += response.token.text
        yield [(prompt, output)]
    gr.Info('Gemma:' + output)
    history.append((prompt, output))
    yield history

@torch.no_grad()
def getImageDescription(image):
    message = "Generate a product title for the image"
    gr.Info('Starting...' + message)
    stop = StopOnTokens()
    messages = [{"role": "system", "content": "You are a helpful assistant."}]
    
    messages.append({"role": "user", "content": message})

    model_inputs = processor.tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        return_tensors="pt"
    )

    image = (
            processor.feature_extractor(image)
            .unsqueeze(0)
    )

    attention_mask = torch.ones(
        1, model_inputs.shape[1] + processor.num_image_latents - 1
    )
    
    model_inputs = {
        "input_ids": model_inputs,
        "images": image,
        "attention_mask": attention_mask
    }

    model_inputs = {k: v.to(device) for k, v in model_inputs.items()}

    streamer = TextIteratorStreamer(processor.tokenizer, timeout=30., skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=1024,
        stopping_criteria=StoppingCriteriaList([stop])
        )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    partial_response = ""
    for new_token in streamer:
        partial_response += new_token
    gr.Info('Got:' + partial_response)
    return partial_response

def resize_image(image):
    image = image.convert('RGB')
    model_input_size = (1024, 1024)
    image = image.resize(model_input_size, Image.BILINEAR)
    return image


def process(image):
    # prepare input
    orig_image = image
    w,h = orig_im_size = orig_image.size
    image = resize_image(orig_image)
    im_np = np.array(image)
    im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1)
    im_tensor = torch.unsqueeze(im_tensor,0)
    im_tensor = torch.divide(im_tensor,255.0)
    im_tensor = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0])
    if torch.cuda.is_available():
        im_tensor=im_tensor.cuda()

    #inference
    result=net(im_tensor)
    # post process
    result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
    ma = torch.max(result)
    mi = torch.min(result)
    result = (result-mi)/(ma-mi)    
    # image to pil
    im_array = (result*255).cpu().data.numpy().astype(np.uint8)
    pil_im = Image.fromarray(np.squeeze(im_array))
    # paste the mask on the original image
    new_im = Image.new("RGBA", pil_im.size, (0,0,0,0))
    new_im.paste(orig_image, mask=pil_im)
    # new_orig_image = orig_image.convert('RGBA')

    return new_im


title = """<h1 style="text-align: center;">Product description generator</h1>"""
css = """
div#col-container {
    margin: 0 auto;
    max-width: 840px;
}
"""

with gr.Blocks(css=css) as demo:
    gr.HTML(title)
    with gr.Row():
        with gr.Column(elem_id="col-container"):
            image = gr.Image(type="pil")
            chat = gr.Chatbot(show_label=False)
            submit = gr.Button(value="Upload", variant="primary")
        with gr.Column():
            output = gr.Image(type="pil", interactive=False)
        
    response_handler = (
        getProductDetails,
        [chat, image],
        [chat]
    )

    background_remover_handler = (
        process,
        [image],
        [output]
    )

    # postresponse_handler = (
    #     lambda: (gr.Button(visible=False), gr.Button(visible=True)),
    #     None,
    #     [submit]
    # )

    event = submit.click(*response_handler)
    event2 = submit.click(*background_remover_handler)
    # event.then(*postresponse_handler)

demo.launch()