FoodIdentifier / app.py
rdezwart's picture
Tidy up some loose ends
26352f5
from threading import Thread
import gradio as gr
import torch
from PIL import Image
from transformers import PreTrainedModel # for type hint
from transformers import TextIteratorStreamer, AutoModelForCausalLM, AutoTokenizer # Moondream
from transformers import YolosImageProcessor, YolosForObjectDetection # YOLOS-small-300
# --- YOLOS --- #
yolos_id = "hustvl/yolos-small-300"
yolos_processor: YolosImageProcessor = YolosImageProcessor.from_pretrained(yolos_id)
yolos_model: YolosForObjectDetection = YolosForObjectDetection.from_pretrained(yolos_id)
# --- Moondream --- #
# 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_model: PreTrainedModel = AutoModelForCausalLM.from_pretrained(
moondream_id, trust_remote_code=True, revision=moondream_revision
)
moondream_model.eval()
def answer_question(img, prompt):
"""
Submits an image and prompt to the Moondream model.
:param img:
:param prompt:
:return: yields the output buffer string
"""
image_embeds = moondream_model.encode_image(img)
streamer = TextIteratorStreamer(moondream_tokenizer, skip_special_tokens=True)
thread = Thread(
target=moondream_model.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()
def detect_objects(img: Image.Image):
"""
Submits an image to the YOLOS-Small-300 model for object detection.
:param img:
:return:
"""
inputs = yolos_processor(img, return_tensors="pt")
outputs = yolos_model(**inputs)
target_sizes = torch.tensor([tuple(reversed(img.size))])
results = yolos_processor.post_process_object_detection(outputs, threshold=0.7, target_sizes=target_sizes)[0]
box_images = []
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
print(
f"Detected {yolos_model.config.id2label[label.item()]} with confidence "
f"{round(score.item(), 3)} at location {box}"
)
box_images.append((
img.crop((box[0], box[1], box[2], box[3])),
f"{yolos_model.config.id2label[label.item()]} ({round(score.item(), 3)})")
)
box_images.append((img, f"original"))
return box_images
def get_selected_index(evt: gr.SelectData) -> int:
"""
Listener for the gallery selection event.
:return: index of the currently selected image
"""
return evt.index
def to_moondream(images: list[tuple[Image.Image, str | None]], index: int) -> tuple[gr.Tabs, Image.Image]:
"""
Listener that sends selected gallery image to the moondream model.
:param images: list of images from yolos_gallery
:param index: index of selected gallery image
:return: selected tab and selected image (no caption)
"""
return gr.Tabs(selected='moondream'), images[index][0]
def enable_button() -> gr.Button:
"""
Helper function for Gradio event listeners.
:return: a button with ``interactive=True`` and ``variant="primary"``
"""
return gr.Button(interactive=True, variant="primary")
def disable_button() -> gr.Button:
"""
Helper function for Gradio event listeners.
:return: a button with ``interactive=False`` and ``variant="secondary"``
"""
return gr.Button(interactive=False, variant="secondary")
if __name__ == "__main__":
with gr.Blocks() as app:
gr.Markdown(
"""
# Food Identifier
Final project for IAT 481 at Simon Fraser University, Spring 2024.
**Models used:**
- [hustvl/yolos-small-300](https://huggingface.co/hustvl/yolos-small-300)
- [vikhyatk/moondream2](https://huggingface.co/vikhyatk/moondream2)
"""
)
selected_image = gr.Number(visible=False, precision=0)
# Referenced: https://github.com/gradio-app/gradio/issues/7726#issuecomment-2028051431
with gr.Tabs() as tabs:
with gr.Tab("Object Detection", id='yolos'):
with gr.Row(equal_height=False):
with gr.Column():
yolos_submit = gr.Button("Detect Objects", interactive=False)
yolos_input = gr.Image(label="Input Image", type="pil", interactive=True, mirror_webcam=False)
with gr.Column():
proceed_button = gr.Button("Select for Captioning", interactive=False)
yolos_gallery = gr.Gallery(label="Detected Objects", object_fit="scale-down", columns=3,
show_share_button=False, selected_index=None, allow_preview=False,
type="pil", interactive=False)
with gr.Tab("Captioning", id='moondream'):
with gr.Row(equal_height=False):
with gr.Column():
with gr.Group():
moon_prompt = gr.Textbox(label="Ask a question about the image:",
value="What is this food item? Include any text on labels.")
moon_submit = gr.Button("Submit", interactive=False)
moon_img = gr.Image(label="Image", type="pil", interactive=True, mirror_webcam=False)
moon_output = gr.TextArea(label="Answer", interactive=False)
# --- YOLOS --- #
yolos_input.upload(enable_button, None, yolos_submit)
yolos_input.clear(disable_button, None, yolos_submit)
yolos_submit.click(detect_objects, yolos_input, yolos_gallery)
yolos_gallery.select(get_selected_index, None, selected_image)
yolos_gallery.select(enable_button, None, proceed_button)
proceed_button.click(to_moondream, [yolos_gallery, selected_image], [tabs, moon_img])
proceed_button.click(enable_button, None, moon_submit)
# --- Moondream --- #
moon_img.upload(enable_button, None, moon_submit)
moon_img.clear(disable_button, None, moon_submit)
moon_submit.click(answer_question, [moon_img, moon_prompt], moon_output)
app.queue().launch()