app.py

import tempfile
from share_btn import community_icon_html, loading_icon_html, share_js
import huggingface_hub
import gradio as gr
from fromage import utils
from fromage import models
import matplotlib.pyplot as plt
from PIL import Image
import torch
import numpy as np
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "False"


css = """
    #share-btn-container {
        display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
        margin-top: 10px;
        margin-left: auto;
        flex: unset;
    }
    #share-btn {
        all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
    }
    #share-btn * {
        all: unset;
    }
    #share-btn-container div:nth-child(-n+2){
        width: auto !important;
        min-height: 0px !important;
    }
    #share-btn-container .wrap {
        display: none !important;
    }
"""

# Download model from HF Hub.
ckpt_path = huggingface_hub.hf_hub_download(
    repo_id='jykoh/fromage', filename='pretrained_ckpt.pth.tar')
args_path = huggingface_hub.hf_hub_download(
    repo_id='jykoh/fromage', filename='model_args.json')
model = models.load_fromage('./', args_path, ckpt_path)


def upload_image(state, image_input):
    conversation = state[0]
    chat_history = state[1]
    conversation += [(f"![](/file={image_input.name})", "")]
    input_image = Image.open(image_input.name).resize(
        (224, 224)).convert('RGB')
    return [conversation, chat_history, input_image], conversation


def reset():
    return [[], [], None], []


def reset_last(state):
    conversation = state[0][:-1]
    chat_history = state[1][:-2]
    input_image = state[2]
    return [conversation, chat_history, input_image], conversation


def save_image_to_local(image: Image.Image):
    # TODO(jykoh): Update so the url path is used, to prevent repeat saving.
    filename = next(tempfile._get_candidate_names()) + '.png'
    image.save(filename)
    return filename


def generate_for_prompt(input_text, state, ret_scale_factor, max_nm_rets, num_words, temperature):
    input_prompt = 'Q: ' + input_text + '\nA:'
    conversation = state[0]
    chat_history = state[1]
    input_image = state[2]
    print('Generating for', chat_history, flush=True)

    # If an image was uploaded, prepend it to the model.
    model_inputs = None
    if input_image is not None:
        model_inputs = chat_history + [input_image]
    else:
        model_inputs = chat_history

    model_inputs.append(input_prompt)

    top_p = 1.0
    if temperature != 0.0:
        top_p = 0.95

    print('Running model.generate_for_images_and_texts with',
          model_inputs, flush=True)
    model_outputs = model.generate_for_images_and_texts(model_inputs,
                                                        num_words=max(num_words, 1), ret_scale_factor=ret_scale_factor, top_p=top_p,
                                                        temperature=temperature, max_num_rets=max_nm_rets)
    print('model_outputs', model_outputs, flush=True)

    im_names = []
    response = ''
    text_outputs = []
    for output in model_outputs:
        if type(output) == str:
            text_outputs.append(output)
            response += output
        elif type(output) == list:
            for image in output:
                filename = save_image_to_local(image)
                response += f'<img src="/file={filename}">'
        elif type(output) == Image.Image:
            filename = save_image_to_local(output)
            response += f'<img src="/file={filename}">'

    # TODO(jykoh): Persist image inputs.
    chat_history = model_inputs + \
        [' '.join([s for s in model_outputs if type(s) == str]) + '\n']
    # Remove [RET] from outputs.
    conversation.append((input_text, response.replace('[RET]', '')))

    # Set input image to None.
    print('state', state, flush=True)
    print('updated state', [conversation, chat_history, None], flush=True)
    return [conversation, chat_history, None], conversation, gr.update(visible=True)


with gr.Blocks(css=css) as demo:
    gr.Markdown(
        '### Grounding Language Models to Images for Multimodal Generation'
    )

    gr.HTML("""
        For faster inference without waiting in queue, you may duplicate the space and use your own GPU. <a href="https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation?duplicate=true"><img style="margin-top: 0em; margin-bottom: 0em" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>
    """)

    chatbot = gr.Chatbot(elem_id="chatbot")
    gr_state = gr.State([[], [], None])  # chat_history, input_image
    with gr.Row(visible=False) as share_group:
        with gr.Group(elem_id="share-btn-container"):
            community_icon = gr.HTML(community_icon_html)
            loading_icon = gr.HTML(loading_icon_html)
            share_button = gr.Button("Share to community", elem_id="share-btn")

    with gr.Row():
        with gr.Column(scale=0.3, min_width=100):
            ret_scale_factor = gr.Slider(minimum=0.0, maximum=3.0, value=1.0, step=0.1, interactive=True,
                                         label="Multiplier for returning images (higher means more frequent)")
            max_ret_images = gr.Number(
                minimum=0, maximum=3, value=1, precision=1, interactive=True, label="Max images to return")
            gr_max_len = gr.Slider(minimum=1, maximum=64, value=32,
                                   step=1, interactive=True, label="Max # of words returned")
            gr_temperature = gr.Slider(
                minimum=0.0, maximum=1.0, value=0.0, interactive=True, label="Temperature")

        with gr.Column(scale=0.7, min_width=400):
            image_btn = gr.UploadButton("🖼️ Image Input", file_types=["image"])
            text_input = gr.Textbox(
                label="Chat Input", lines=1, placeholder="Upload an image above [optional]. Then enter a text prompt, and press enter!")

            with gr.Row():
                with gr.Column(scale=0.33):
                    submit_btn = gr.Button(
                        "Submit", interactive=True, variant="primary")
                with gr.Column(scale=0.33):
                    clear_last_btn = gr.Button("Clear Last Round")
                with gr.Column(scale=0.33):
                    clear_btn = gr.Button("Clear All")

    text_input.submit(generate_for_prompt, [text_input, gr_state, ret_scale_factor,
                      max_ret_images, gr_max_len, gr_temperature], [gr_state, chatbot, share_group])
    text_input.submit(lambda: "", None, text_input)  # Reset chatbox.
    submit_btn.click(generate_for_prompt, [text_input, gr_state, ret_scale_factor,
                     max_ret_images, gr_max_len, gr_temperature], [gr_state, chatbot, share_group])
    submit_btn.click(lambda: "", None, text_input)  # Reset chatbox.

    image_btn.upload(upload_image, [gr_state, image_btn], [gr_state, chatbot])
    clear_last_btn.click(reset_last, [gr_state], [gr_state, chatbot])
    clear_btn.click(reset, [], [gr_state, chatbot])
    share_button.click(None, [], [], _js=share_js)


demo.queue(concurrency_count=1, api_open=False, max_size=16)
demo.launch(debug=True, server_name="0.0.0.0")