Spaces:
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Running
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Zero
File size: 8,686 Bytes
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import gradio as gr
from inference import inference_and_run
import spaces
import os
import re
import shutil
model_name = 'Ferret-UI'
cur_dir = os.path.dirname(os.path.abspath(__file__))
@spaces.GPU()
def inference_with_gradio(chatbot, image, prompt, model_path, box=None, temperature=0.2, top_p=0.7, max_new_tokens=512):
dir_path = os.path.dirname(image)
# image_path = image
# Define the directory where you want to save the image (current directory)
filename = os.path.basename(image)
dir_path = "./"
# Create the new path for the file (in the current directory)
image_path = os.path.join(dir_path, filename)
shutil.copy(image, image_path)
print("filename path: ", filename)
if "gemma" in model_path.lower():
conv_mode = "ferret_gemma_instruct"
else:
conv_mode = "ferret_llama_3"
# inference_text = inference_and_run(
# image_path=image_path,
# prompt=prompt,
# conv_mode=conv_mode,
# model_path=model_path,
# box=box
# )
inference_text = inference_and_run(
image_path=filename, # double check this
image_dir=dir_path,
prompt=prompt,
model_path="jadechoghari/Ferret-UI-Gemma2b",
conv_mode=conv_mode,
temperature=temperature,
top_p=top_p,
box=box,
max_new_tokens=max_new_tokens,
# stop=stop # Assuming we want to process the image
)
# print("done, now appending", inference_text)
# chatbot.append((prompt, inference_text))
# return chatbot
# Convert inference_text to string if it's not already
if isinstance(inference_text, (list, tuple)):
inference_text = str(inference_text[0])
# Update chatbot history with new message pair
new_history = chatbot.copy() if chatbot else []
new_history.append((prompt, inference_text))
return new_history
def submit_chat(chatbot, text_input):
response = ''
# chatbot.append((text_input, response))
return chatbot, ''
def clear_chat():
return [], None, "", "", 0.2, 0.7, 512
with open(f"{cur_dir}/logo.svg", "r", encoding="utf-8") as svg_file:
svg_content = svg_file.read()
font_size = "2.5em"
svg_content = re.sub(r'(<svg[^>]*)(>)', rf'\1 height="{font_size}" style="vertical-align: middle; display: inline-block;"\2', svg_content)
html = f"""
<p align="center" style="font-size: {font_size}; line-height: 1;">
<span style="display: inline-block; vertical-align: middle;">{svg_content}</span>
<span style="display: inline-block; vertical-align: middle;">{model_name}</span>
</p>
<center><font size=3><b>{model_name}</b> Demo: Upload an image, provide a prompt, and get insights using advanced AI models. <a href='https://huggingface.co/jadechoghari/Ferret-UI-Gemma2b'>π Huggingface</a></font></center>
"""
with open(f"{cur_dir}/ferretui_icon.png", "rb") as image_file:
image_data = image_file.read()
# html = f"""
# <p align="center">
# <img src='data:image/png;base64,{image_data.encode("base64").decode("utf-8")}' alt='Ferret-UI' style='width: 100px; vertical-align: middle; border-radius: 15px; box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);'/>
# <span style="font-size: 2em; font-weight: bold; margin-left: 10px; vertical-align: middle;">{model_name}</span>
# </p>
# <center><font size=3><b>{model_name}</b> Demo: Upload an image, provide a prompt, and get insights using advanced AI models. <a href='https://huggingface.co/jadechoghari/Ferret-UI-Gemma2b'>π Huggingface</a></font></center>
# """
html = f"""
<div style="text-align: center; padding: 20px;">
<div style="display: inline-block; background-color: #f5f5f7; padding: 20px; border-radius: 20px; box-shadow: 0px 6px 20px rgba(0, 0, 0, 0.1);">
<div style="display: flex; align-items: center;">
<img src='https://github.com/apple/ml-ferret/blob/main/ferretui/figs/ferretui_icon.png?raw=true' alt='Ferret-UI'
style='width: 80px; height: 80px; border-radius: 20px; box-shadow: 0px 8px 16px rgba(0, 0, 0, 0.2);'/>
<div style="margin-left: 15px;">
<h1 style="font-size: 2.8em; font-family: -apple-system, BlinkMacSystemFont, sans-serif; color: #1D1D1F;
font-weight: bold; margin-bottom: 0;">ο£Ώ {model_name}</h1>
<p style="font-size: 1.2em; color: #6e6e73; font-family: -apple-system, BlinkMacSystemFont, sans-serif; margin-top: 5px;">
π± Grounded Mobile UI Understanding with Multimodal LLMs.<br>
A new MLLM tailored for enhanced understanding of mobile UI screens, equipped with referring, grounding, and reasoning capabilities.
</p>
<a href='https://huggingface.co/jadechoghari/Ferret-UI-Gemma2b' style='text-decoration: none;'>
<button style="background-color: #007aff; color: white; font-size: 1.2em; padding: 10px 20px; border-radius: 10px; border: none; margin-top: 10px; box-shadow: 0px 4px 12px rgba(0, 122, 255, 0.4); cursor: pointer;">
π€ Try on Hugging Face
</button>
</a>
</div>
</div>
</div>
<p style="font-size: 1.2em; color: #86868B; font-family: -apple-system, BlinkMacSystemFont, sans-serif; margin-top: 30px;">
We release two Ferret-UI checkpoints, built on gemma-2b and Llama-3-8B models respectively, for public exploration. π
</p>
</div>
"""
latex_delimiters_set = [{
"left": "\\(",
"right": "\\)",
"display": False
}, {
"left": "\\begin{equation}",
"right": "\\end{equation}",
"display": True
}, {
"left": "\\begin{align}",
"right": "\\end{align}",
"display": True
}]
# Set up UI components
image_input = gr.Image(label="Upload Image", type="filepath", height=350)
text_input = gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
model_dropdown = gr.Dropdown(choices=[
"jadechoghari/Ferret-UI-Gemma2b",
"jadechoghari/Ferret-UI-Llama8b",
], label="Model Path", value="jadechoghari/Ferret-UI-Gemma2b")
bounding_box_input = gr.Textbox(placeholder="Optional bounding box (x1, y1, x2, y2)", label="Bounding Box (optional)")
# Adding Sliders for temperature, top_p, and max_new_tokens
temperature_input = gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=0.2, label="Temperature")
top_p_input = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.7, label="Top P")
max_new_tokens_input = gr.Slider(minimum=1, maximum=1024, step=1, value=512, label="Max New Tokens")
chatbot = gr.Chatbot(label="Chat with Ferret-UI", height=400, show_copy_button=True, latex_delimiters=latex_delimiters_set)
with gr.Blocks(title=model_name, theme=gr.themes.Ocean()) as demo:
gr.HTML(html)
with gr.Row():
with gr.Column(scale=3):
image_input.render()
text_input.render()
model_dropdown.render()
bounding_box_input.render()
temperature_input.render() # Render temperature input
top_p_input.render() # Render top_p input
max_new_tokens_input.render()
gr.Examples(
examples=[
["appstore_reminders.png", "Describe the image in details", "jadechoghari/Ferret-UI-Gemma2b", None],
["appstore_reminders.png", "What's inside the selected region?", "jadechoghari/Ferret-UI-Gemma2b", "189, 906, 404, 970"],
["appstore_reminders.png", "Where is the Game Tab?", "jadechoghari/Ferret-UI-Gemma2b", None],
],
inputs=[image_input, text_input, model_dropdown, bounding_box_input]
)
with gr.Column(scale=7):
chatbot.render()
with gr.Row():
send_btn = gr.Button("Send", variant="primary")
clear_btn = gr.Button("Clear", variant="secondary")
send_click_event = send_btn.click(
inference_with_gradio, [chatbot, image_input, text_input, model_dropdown, bounding_box_input, temperature_input, top_p_input, max_new_tokens_input], chatbot
).then(submit_chat, [chatbot, text_input], [chatbot, text_input])
submit_event = text_input.submit(
inference_with_gradio, [chatbot, image_input, text_input, model_dropdown, bounding_box_input, temperature_input, top_p_input, max_new_tokens_input], chatbot
).then(submit_chat, [chatbot, text_input], [chatbot, text_input])
clear_btn.click(clear_chat, outputs=[chatbot, image_input, text_input, bounding_box_input, temperature_input, top_p_input, max_new_tokens_input])
demo.launch() |