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import gradio as gr
from transformers import 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, code_revision=moondream_revision)
moondream_model = AutoModelForCausalLM.from_pretrained(
moondream_id, trust_remote_code=True, code_revision=moondream_revision
)
def answer_question(_img, _prompt):
image_embeds = moondream_model.encode_image(_img)
return moondream_model.answer_question(image_embeds, _prompt, moondream_tokenizer)
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)
prompt.submit(answer_question, [img, prompt], output)
if __name__ == "__main__":
app.launch()
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