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Running
on
Zero
Running
on
Zero
File size: 5,071 Bytes
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import gradio as gr
import requests
from PIL import Image
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
import spaces
@spaces.GPU
def infer_infographics(image, question):
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base").to("cuda")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs)
return processor.decode(predictions[0], skip_special_tokens=True)
@spaces.GPU
def infer_ui(image, question):
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-screen2words-base").to("cuda")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-screen2words-base")
inputs = processor(images=image,text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs)
return processor.decode(predictions[0], skip_special_tokens=True)
@spaces.GPU
def infer_chart(image, question):
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-chartqa-base").to("cuda")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-chartqa-base")
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs)
return processor.decode(predictions[0], skip_special_tokens=True)
@spaces.GPU
def infer_doc(image, question):
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-docvqa-base").to("cuda")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-docvqa-base")
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs)
return processor.decode(predictions[0], skip_special_tokens=True)
css = """
#mkd {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.HTML("<h1><center>Pix2Struct 📄<center><h1>")
gr.HTML("<h3><center>Pix2Struct is a powerful backbone for visual question answering. ⚡</h3>")
gr.HTML("<h3><center>Each tab in this app demonstrates Pix2Struct models fine-tuned on document question answering, infographics question answering, question answering on user interfaces, and charts. 📄📱📊<h3>")
gr.HTML("<h3><center>This app has base versions of each model. For better performance, use large checkpoints.<h3>")
with gr.Tab(label="Visual Question Answering over Documents"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Document")
question = gr.Text(label="Question")
submit_btn = gr.Button(label="Submit")
output = gr.Text(label="Answer")
gr.Examples(
[["docvqa_example.png", "How many items are sold?"]],
inputs = [input_img, question],
outputs = [output],
fn=infer_doc,
cache_examples=True,
label='Click on any Examples below to get Document Question Answering results quickly 👇'
)
submit_btn.click(infer_doc, [input_img, question], [output])
with gr.Tab(label="Visual Question Answering over Infographics"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Image")
question = gr.Text(label="Question")
submit_btn = gr.Button(label="Submit")
output = gr.Text(label="Answer")
gr.Examples(
[["infographics_example.jpeg", "What is this infographic about?"]],
inputs = [input_img, question],
outputs = [output],
fn=infer_doc,
cache_examples=True,
label='Click on any Examples below to get Infographics QA results quickly 👇'
)
submit_btn.click(infer_infographics, [input_img, question], [output])
with gr.Tab(label="Caption User Interfaces"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input UI Image")
question = gr.Text(label="Question")
submit_btn = gr.Button(label="Submit")
output = gr.Text(label="Caption")
submit_btn.click(infer_chart, [input_img, question], [output])
gr.Examples(
[["screen2words_ui_example.png", "What is this UI about?"]],
inputs = [input_img, question],
outputs = [output],
fn=infer_doc,
cache_examples=True,
label='Click on any Examples below to get UI question answering results quickly 👇'
)
with gr.Tab(label="Ask about Charts"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Chart")
question = gr.Text(label="Question")
submit_btn = gr.Button(label="Submit")
output = gr.Text(label="Caption")
submit_btn.click(infer_chart, [input_img, question], [output])
gr.Examples(
[["chartqa_example.png", "How much percent is bicycle?"]],
inputs = [input_img, question],
outputs = [output],
fn=infer_doc,
cache_examples=True,
label='Click on any Examples below to get Chart question answering results quickly 👇'
)
demo.launch(debug=True) |