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| from threading import Thread | |
| from PIL import Image | |
| import gradio as gr | |
| import torch | |
| from transformers import PreTrainedModel, AutoImageProcessor # for type hint | |
| from transformers import TextIteratorStreamer, AutoModelForCausalLM, AutoTokenizer # Moondream | |
| from transformers import YolosImageProcessor, YolosForObjectDetection # YOLOS-small-300 | |
| # --- 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() | |
| # --- YOLOS --- # | |
| yolos_id = "hustvl/yolos-small-300" | |
| yolos_processor: YolosImageProcessor = YolosImageProcessor.from_pretrained(yolos_id) | |
| yolos_model: YolosForObjectDetection = YolosForObjectDetection.from_pretrained(yolos_id) | |
| 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): | |
| 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] | |
| 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}" | |
| ) | |
| 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.Tab("Object Detection"): | |
| with gr.Row(): | |
| yolos_input = gr.Image(type="pil") | |
| yolos_output = gr.Image() | |
| yolos_button = gr.Button("Submit") | |
| with gr.Tab("Inference"): | |
| with gr.Row(): | |
| moon_prompt = gr.Textbox(label="Input", value="Describe this image.") | |
| moon_submit = gr.Button("Submit") | |
| with gr.Row(): | |
| moon_img = gr.Image(label="Image", type="pil") | |
| moon_output = gr.TextArea(label="Output") | |
| moon_submit.click(answer_question, [moon_img, moon_prompt], moon_output) | |
| yolos_button.click(detect_objects, [yolos_input]) | |
| app.queue().launch() | |