| 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() |