Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,298 Bytes
b802c2a 34d659e 8df48e3 b802c2a e010032 b802c2a b872a0b b802c2a b872a0b b802c2a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
import gradio as gr
import requests
from PIL import Image
from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
import spaces
@spaces.GPU
def infer_diagram(image, question):
model = PaliGemmaForConditionalGeneration.from_pretrained("google/paligemma-3b-ft-ai2d-448").to("cuda")
processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-ft-ai2d-448")
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs, max_new_tokens=100)
return processor.decode(predictions[0], skip_special_tokens=True)[len(question):].lstrip("\n")
@spaces.GPU
def infer_ocrvqa(image, question):
model = PaliGemmaForConditionalGeneration.from_pretrained("google/paligemma-3b-ft-ocrvqa-896").to("cuda")
processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-ft-ocrvqa-896")
inputs = processor(images=image,text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs, max_new_tokens=100)
return processor.decode(predictions[0], skip_special_tokens=True)[len(question):].lstrip("\n")
@spaces.GPU
def infer_infographics(image, question):
model = PaliGemmaForConditionalGeneration.from_pretrained("google/paligemma-3b-ft-infovqa-896").to("cuda")
processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-ft-infovqa-896")
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs, max_new_tokens=100)
return processor.decode(predictions[0], skip_special_tokens=True)[len(question):].lstrip("\n")
@spaces.GPU
def infer_doc(image, question):
model = PaliGemmaForConditionalGeneration.from_pretrained("google/paligemma-3b-ft-docvqa-896").to("cuda")
processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-ft-docvqa-896")
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs, max_new_tokens=100)
return processor.decode(predictions[0], skip_special_tokens=True)[len(question):].lstrip("\n")
css = """
#mkd {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.HTML("<h1><center>PaliGemma Fine-tuned on Documents 📄<center><h1>")
gr.HTML("<h3><center>This Space is built for you to compare different PaliGemma models fine-tuned on document tasks. ⚡</h3>")
gr.HTML("<h3><center>Each tab in this app demonstrates PaliGemma models fine-tuned on document question answering, infographics question answering, diagram understanding, and reading comprehension from images. 📄📕📊<h3>")
gr.HTML("<h3><center>Models are downloaded on the go, so first inference in each tab might take time if it's not already downloaded.<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(value="Submit")
output = gr.Text(label="Answer")
gr.Examples(
[["assets/docvqa_example.png", "How many items are sold?"]],
inputs = [input_img, question],
outputs = [output],
fn=infer_doc,
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(value="Submit")
output = gr.Text(label="Answer")
gr.Examples(
[["assets/infographics_example (1).jpeg", "What is this infographic about?"]],
inputs = [input_img, question],
outputs = [output],
fn=infer_infographics,
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="Reading from Images"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Document")
question = gr.Text(label="Question")
submit_btn = gr.Button(value="Submit")
output = gr.Text(label="Infer")
submit_btn.click(infer_ocrvqa, [input_img, question], [output])
gr.Examples(
[["assets/ocrvqa.jpg", "Who is the author of this book?"]],
inputs = [input_img, question],
outputs = [output],
fn=infer_doc,
label='Click on any Examples below to get image reading comprehension results quickly 👇'
)
with gr.Tab(label="Diagram Understanding"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Diagram")
question = gr.Text(label="Question")
submit_btn = gr.Button(value="Submit")
output = gr.Text(label="Infer")
submit_btn.click(infer_diagram, [input_img, question], [output])
gr.Examples(
[["assets/diagram.png", "What is the diagram showing?"]],
inputs = [input_img, question],
outputs = [output],
fn=infer_doc,
label='Click on any Examples below to get diagram understanding results quickly 👇'
)
demo.launch(debug=True) |