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from threading import Thread
import gradio as gr
from transformers import PreTrainedModel
from transformers import TextIteratorStreamer, AutoModelForCausalLM, AutoTokenizer
# Moondream does not support the HuggingFace pipeline system, so we have to do it manually
moondream_id = "vikhyatk/moondream2"
moondream_revision = "2024-04-02"
moondream_tokenizer = AutoTokenizer.from_pretrained(moondream_id, revision=moondream_revision)
moondream: PreTrainedModel = AutoModelForCausalLM.from_pretrained(
moondream_id, trust_remote_code=True, revision=moondream_revision
)
moondream.eval()
def answer_question(_img, _prompt):
image_embeds = moondream.encode_image(_img)
streamer = TextIteratorStreamer(moondream_tokenizer, skip_special_tokens=True)
thread = Thread(
target=moondream.answer_question,
kwargs={
"image_embeds": image_embeds,
"question": _prompt,
"tokenizer": moondream_tokenizer,
"streamer": streamer,
},
)
thread.start()
buffer = ""
for new_text in streamer:
buffer += new_text
yield buffer.strip()
if __name__ == "__main__":
with gr.Blocks() as app:
gr.Markdown(
"""
# Food Identifier
Final project for IAT 481 at Simon Fraser University, Spring 2024.
"""
)
with gr.Row():
prompt = gr.Textbox(label="Input", value="Describe this image.")
submit = gr.Button("Submit")
with gr.Row():
img = gr.Image(label="Image", type="pil")
output = gr.TextArea(label="Output")
submit.click(answer_question, [img, prompt], output)
app.queue().launch()
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