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Zero
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"""SpaceLlama3.1 demo gradio app."""
import datetime
import logging
import os
import gradio as gr
import torch
import PIL.Image
from prismatic import load
from huggingface_hub import login
# Authenticate with the Hugging Face Hub
def authenticate_huggingface():
hf_token = os.getenv("HF_TOKEN")
if hf_token:
login(token=hf_token)
else:
raise ValueError("Hugging Face API token not found. Please set it as an environment variable named 'HF_TOKEN'.")
# Call the authentication function once at the start
authenticate_huggingface()
INTRO_TEXT = """SpaceLlama3.1 demo\n\n
| [Model](https://huggingface.co/remyxai/SpaceLlama3.1)
| [GitHub](https://github.com/remyxai/VQASynth/tree/main)
| [Demo](https://huggingface.co/spaces/remyxai/SpaceLlama3.1)
| [Discord](https://discord.gg/DAy3P5wYJk)
\n\n
**This is an experimental research model.** Make sure to add appropriate guardrails when using the model for applications.
"""
def compute(image, prompt, model_location):
"""Runs model inference."""
if image is None:
raise gr.Error("Image required")
logging.info('prompt="%s"', prompt)
# Open the image file
if isinstance(image, str):
image = PIL.Image.open(image).convert("RGB")
# Set device and load the model
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
vlm = load(model_location) # No need to pass the token again
vlm.to(device, dtype=torch.bfloat16)
# Prepare prompt
prompt_builder = vlm.get_prompt_builder()
prompt_builder.add_turn(role="human", message=prompt)
prompt_text = prompt_builder.get_prompt()
# Generate the text based on image and prompt
generated_text = vlm.generate(
image,
prompt_text,
do_sample=True,
temperature=0.1,
max_new_tokens=512,
min_length=1,
)
output = generated_text.split("</s>")[0]
logging.info('output="%s"', output)
return output
def reset():
"""Resets the input fields."""
return "", None
def create_app():
"""Creates demo UI."""
with gr.Blocks() as demo:
# Main UI structure
gr.Markdown(INTRO_TEXT)
with gr.Row():
image = gr.Image(value=None, label="Image", type="filepath", visible=True) # input
with gr.Column():
prompt = gr.Textbox(value="", label="Prompt", visible=True)
model_info = gr.Markdown(label="Model Info")
run = gr.Button("Run", variant="primary")
clear = gr.Button("Clear")
highlighted_text = gr.HighlightedText(value="", label="Output", visible=True)
# Model location
model_location = "remyxai/SpaceLlama3.1" # Update as needed
# Button event handlers
run.click(
fn=compute,
inputs=[image, prompt, model_location],
outputs=highlighted_text,
)
clear.click(fn=reset, inputs=None, outputs=[prompt, image])
# Status
status = gr.Markdown(f"Startup: {datetime.datetime.now()}")
gpu_kind = gr.Markdown(f"GPU=?")
demo.load(
fn=lambda: [f"Model `{model_location}` loaded."],
inputs=None,
outputs=model_info,
)
return demo
if __name__ == "__main__":
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
for k, v in os.environ.items():
logging.info('environ["%s"] = %r', k, v)
create_app().queue().launch()
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