app.py

import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "False"
import numpy as np
import torch
from PIL import Image
import matplotlib.pyplot as plt

from fromage import models
from fromage import utils
import gradio as gr
import huggingface_hub
import tempfile


class FromageChatBot:
    def __init__(self):
        # 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')
        self.model = models.load_fromage('./', args_path, ckpt_path)
        self.chat_history = ''
        self.input_image = None


    def reset(self):
        self.chat_history = ""
        self.input_image = None
        return [], []


    def upload_image(self, state, image_input):
        state += [(f"![](/file={image_input.name})", "(Image received. Type or ask something to continue.)")]
        self.input_image = Image.open(image_input.name).resize((224, 224)).convert('RGB')
        return state, state


    def save_image_to_local(self, 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(self, input_text, state, ret_scale_factor, max_nm_rets, num_words, temperature):
        input_prompt = 'Q: ' + input_text + '\nA:'
        self.chat_history += input_prompt
        print('Generating for', self.chat_history, flush=True)

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

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

        print('Running model.generate_for_images_and_texts', flush=True)
        model_outputs = self.model.generate_for_images_and_texts(model_inputs, 
            num_words=num_words, 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 = self.save_image_to_local(image)
                    response += f'<img src="/file={filename}">'
            elif type(output) == Image.Image:
                    filename = self.save_image_to_local(output)
                    response += f'<img src="/file={filename}">'

        # TODO(jykoh): Persist image inputs.
        self.chat_history += ' '.join(text_outputs)
        if self.chat_history[-1] != '\n':
            self.chat_history += '\n'
        self.input_image = None

        state.append((input_text, response))
        return state, state


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

            chatbot = gr.Chatbot()
            gr_state = gr.State([])

            with gr.Row():
                with gr.Column(scale=0.3, min_width=0):
                    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.Number(value=32, precision=1, label="Max # of words returned", interactive=True)
                    gr_temperature = gr.Number(value=0.0, label="Temperature", interactive=True)

                with gr.Column(scale=0.7, min_width=0):
                    image_btn = gr.UploadButton("Image Input", file_types=["image"])     
                    text_input = gr.Textbox(label="Text Input", lines=1, placeholder="Upload an image above [optional]. Then enter a text prompt, and press enter!")
                    clear_btn = gr.Button("Clear History")

            text_input.submit(self.generate_for_prompt, [text_input, gr_state, ret_scale_factor, max_ret_images, gr_max_len, gr_temperature], [gr_state, chatbot])
            image_btn.upload(self.upload_image, [gr_state, image_btn], [gr_state, chatbot])
            clear_btn.click(self.reset, [], [gr_state, chatbot])

        demo.launch(share=False, debug=True, server_name="0.0.0.0")


def main():
    chatbot = FromageChatBot()
    chatbot.launch()    


if __name__ == "__main__":
    chatbot = FromageChatBot()
    chatbot.launch()