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#!/usr/bin/env python
from __future__ import annotations
import os
import pathlib
import gradio as gr
from prismer_model import Model
def create_demo():
model = Model()
model.mode = 'vqa'
with gr.Row():
with gr.Column():
image = gr.Image(label='Input', type='filepath')
model_name = gr.Dropdown(label='Model', choices=['Prismer-Base', 'Prismer-Large'], value='Prismer-Base')
question = gr.Text(label='Question')
run_button = gr.Button('Run')
with gr.Column(scale=1.5):
answer = gr.Text(label='Model Prediction')
with gr.Row():
depth = gr.Image(label='Depth')
edge = gr.Image(label='Edge')
normals = gr.Image(label='Normals')
with gr.Row():
segmentation = gr.Image(label='Segmentation')
object_detection = gr.Image(label='Object Detection')
ocr = gr.Image(label='OCR Detection')
inputs = [image, model_name, question]
outputs = [answer, depth, edge, normals, segmentation, object_detection, ocr]
# paths = sorted(pathlib.Path('prismer/images').glob('*'))
# examples = [[path.as_posix(), 'prismer_base'] for path in paths]
# gr.Examples(examples=examples,
# inputs=inputs,
# outputs=outputs,
# fn=model.run_caption,
# cache_examples=os.getenv('SYSTEM') == 'spaces')
paths = sorted(pathlib.Path('prismer/images').glob('*'))
examples = [[path.as_posix(), 'Prismer-Base'] for path in paths]
gr.Examples(examples=examples,
inputs=inputs,
outputs=outputs,
fn=model.run_vqa_model)
run_button.click(fn=model.run_vqa_model, inputs=inputs, outputs=outputs)
if __name__ == '__main__':
demo = create_demo()
demo.queue().launch()
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