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import gradio as gr | |
from texify.inference import batch_inference | |
from texify.model.model import load_model | |
from texify.model.processor import load_processor | |
from PIL import Image | |
title="""🙋🏻♂️Welcome to🌟Tonic's👨🏻🔬Texify""" | |
description="""You can upload a picture with a math formula and this model will return latex formulas. Texify is a multimodal input model. You can use this Space to test out the current model [vikp/texify2](https://huggingface.co/vikp/texify2) You can also use vikp/texify2🚀 by cloning this space. Simply click here: [Duplicate Space](https://huggingface.co/spaces/Tonic1/texify?duplicate=true) | |
Join us: TeamTonic is always making cool demos! Join our active builder's community on Discord: [Discord](https://discord.gg/nXx5wbX9) On Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On Github: [Polytonic](https://github.com/tonic-ai) & contribute to [PolyGPT](https://github.com/tonic-ai/polygpt-alpha) You can also join the [texify community here](https://discord.gg/zJSDQJWDe8). Big thanks to Vik Paruchuri for the invite and Huggingface for the Community Grant. Your special attentions are much appreciated. | |
""" | |
model = load_model() | |
processor = load_processor() | |
def process_image(img): | |
img = Image.fromarray(img) | |
results = batch_inference([img], model, processor) | |
return '\n'.join(results) if isinstance(results, list) else results | |
iface = gr.Interface( | |
gr.Markdown(title) | |
gr.Markdown(description) | |
fn=process_image, | |
inputs=gr.inputs.Image(type="pil"), | |
outputs="text", | |
if __name__ == "__main__": | |
iface.launch() |