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| # Kept getting "No module named 'fastai'" from huggingface..workaround: | |
| # https://stackoverflow.com/a/50255019 | |
| import subprocess | |
| import sys | |
| def install(package): | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", package]) | |
| install("fastai") | |
| # Errors from within libraries suggest that importing timm must be done before importing fast.ai, something like this: | |
| # https://forums.fast.ai/t/nameerror-name-timm-is-not-defined/96158 | |
| #!pip install -Uqq fastai timm | |
| #!pip install timm | |
| install("timm") | |
| from fastai.vision.all import * | |
| import timm | |
| learn_resnet = load_learner('model - resnet18.pkl') | |
| learn_convnext = load_learner('model - convnext_tiny - pad - 150imgs each - cleaned.pkl') | |
| categories = ('1', '4', '8', '12', '16', '20s', '30s', '40s','50s', '60s', '70s', '80s', '90s') | |
| def classify_image_resnet(img): | |
| return classify_image(learn_resnet, img) | |
| def classify_image_convnext(img): | |
| return classify_image(learn_convnext, img) | |
| def classify_image(learn, img): | |
| tens = tensor(img) #fix apparently needed after fastai 2.7.11 released | |
| pred,idx,probs = learn.predict(tens) | |
| return dict(zip(categories, map(float,probs))) | |
| from fastdownload import download_url | |
| import os | |
| def classify_image_url_resnet(url_text): | |
| return classify_image_url(learn_resnet, url_text) | |
| def classify_image_url_convnext(url_text): | |
| return classify_image_url(learn_convnext, url_text) | |
| def classify_image_url(learn, url_text): | |
| try: | |
| dest = 'temp.jpg' | |
| download_url(url_text, dest, show_progress=False) | |
| im = Image.open(dest) | |
| img = im.to_thumb(256,256) | |
| #resize_images(dest, max_size=400, dest=dest) | |
| os.remove(dest) | |
| return classify_image(learn, img),img | |
| except: | |
| # in case there is any error, invalid URL or invalid image, etc., not sure how Gradio will handle a runtime exception so catching it to be safe | |
| return { categories[0]: 0.0, categories[1]: 0.0 },None | |
| def classify_image_url_debug(url_text): | |
| try: | |
| dest = 'temp.jpg' | |
| download_url(url_text, dest, show_progress=False) | |
| im = Image.open(dest) | |
| img = im.to_thumb(256,256) | |
| #resize_images(dest, max_size=400, dest=dest) | |
| os.remove(dest) | |
| temp = classify_image(learn_resnet, img) | |
| return "Success: " + str(temp) | |
| except Exception as ex: | |
| error = f"{type(ex).__name__} was raised: {ex}" | |
| return error; | |
| import gradio as gr | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown(" ") | |
| gr.Markdown("Rudimentary age predictor. No refinement, just a hacked together experiment to try multiple output classes with fast.ai. Many ways the accuracy could be improved.") | |
| gr.Markdown("Note that training images were taken from DuckDuckGo and results for '[n] year old' were majority women, so accuracy is expected to be reduced for men. (Could easily be updated.)") | |
| gr.Markdown("See: [https://www.kaggle.com/code/zachwormgoor/age-predictor](https://www.kaggle.com/code/zachwormgoor/age-predictor) ") | |
| gr.Markdown(" ") | |
| gr.Markdown("---") | |
| gr.Markdown("Predict age from uploaded image or from provide URL to image file: ") | |
| gr.Markdown(" ") | |
| with gr.Tabs(): | |
| with gr.TabItem("Image - resnet18"): | |
| with gr.Row(): | |
| img_r_input = gr.Image() | |
| img_r_output = gr.outputs.Label() | |
| image_r_button = gr.Button("Predict") | |
| with gr.TabItem("URL - resnet18"): | |
| with gr.Row(): | |
| text_r_input = gr.Textbox() | |
| text_r_output = gr.outputs.Label() | |
| text_r_preview = gr.Image() | |
| url_r_button = gr.Button("Predict") | |
| with gr.TabItem("Image - convnext_tiny"): | |
| with gr.Row(): | |
| img_c_input = gr.Image() | |
| img_c_output = gr.outputs.Label() | |
| image_c_button = gr.Button("Predict") | |
| with gr.TabItem("URL - convnext_tiny"): | |
| with gr.Row(): | |
| text_c_input = gr.Textbox() | |
| text_c_output = gr.outputs.Label() | |
| text_c_preview = gr.Image() | |
| text_c_button = gr.Button("Predict") | |
| with gr.TabItem("URL - debug"): | |
| with gr.Row(): | |
| text_d_input = gr.Textbox() | |
| text_d_output = gr.outputs.Label() | |
| text_d_button = gr.Button("Predict - debug") | |
| image_r_button.click(classify_image_resnet, inputs=img_r_input, outputs=img_r_output) | |
| url_r_button.click( classify_image_url_resnet, inputs=text_r_input, outputs=[text_r_output,text_r_preview]) | |
| image_c_button.click(classify_image_convnext, inputs=img_c_input, outputs=img_c_output) | |
| text_c_button.click( classify_image_url_convnext, inputs=text_c_input, outputs=[text_c_output,text_c_preview]) | |
| text_d_button.click( classify_image_url_debug, inputs=text_d_input, outputs=text_d_output) | |
| demo.launch() | |