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import os
os.system("pip install deepspeed")
os.system("pip freeze")
import gradio as gr
import re
from magma import Magma
from magma.image_input import ImageInput
from huggingface_hub import hf_hub_url, cached_download
checkpoint_url = hf_hub_url(repo_id="osanseviero/magma", filename="model.pt")
checkpoint_path = cached_download(checkpoint_url)
model = Magma.from_checkpoint(
config_path = "configs/MAGMA_v1.yml",
checkpoint_path = checkpoint_path,
device = 'cuda:0'
)
def generate(image,context, length, temperature, top_k):
# context = context.strip()
# url_regex = r'https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)'
# lines = context.split('\n')
# inputs = []
# for line in lines:
# if re.match(url_regex, line):
# try:
# inputs.append(ImageInput(line))
# except Exception as e:
# return str(e)
# else:
# inputs.append(line)
inputs =[
## supports urls and path/to/image
ImageInput(image),
context
]
## returns a tensor of shape: (1, 149, 4096)
embeddings = model.preprocess_inputs(inputs)
## returns a list of length embeddings.shape[0] (batch size)
output = model.generate(
embeddings = embeddings,
max_steps = length,
temperature = (0.01 if temperature == 0 else temperature),
top_k = top_k
)
return output[0]
examples=["woods_hi.jpeg","Describe the painting:",15,0.7,0]
title="MAGMA"
description="MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning"
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.05253'>MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning</a> | <a href='https://github.com/Aleph-Alpha/magma'>Github Repo</a></p>"
iface = gr.Interface(
fn=generate,
inputs=[
[gr.inputs.Image(type="filepath"),gr.inputs.Textbox(
label="Prompt:",
default="Describe the painting:",
lines=7)],
gr.inputs.Slider(minimum=1, maximum=100, default=15, step=1, label="Output tokens:"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.7, label='Temperature'),
gr.inputs.Slider(minimum=0, maximum=100, default=0, step=1, label='Top K')
],
outputs=["textbox"],
examples=examples,
title=title,
description=description,
article=article
).launch(enable_queue=True,cache_examples=True)
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