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import gradio as gr |
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from fastai.vision.all import * |
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import skimage |
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import pathlib |
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import pandas as pd |
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plt = platform.system() |
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if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath |
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title = "Face condition Analyzer" |
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description = "A face condition detector trained on the custom dataset with fastai. Created using Gradio and HuggingFace Spaces." |
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examples = [['harmonal_acne.jpg'],['forehead_wrinkles.jpg'],['oily_skin.jpg']] |
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enable_queue=True |
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with gr.Blocks(title=title,description=description,examples=examples,enable_queue=enable_queue) as demo: |
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learn = load_learner('export.pkl') |
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labels = learn.dls.vocab |
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def predict(img): |
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img = PILImage.create(img) |
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pred,pred_idx,probs = learn.predict(img) |
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return {labels[i]: float(probs[i]) for i in range(len(labels))} |
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gr.Markdown("# Face Skin Analyzer") |
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gr.Markdown("A face condition detector trained on the custom dataset with fastai. Created using Gradio and HuggingFace Spaces. Kindly upload a photo of your face.") |
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with gr.Row(): |
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inputs = gr.inputs.Image(shape=(512, 512)) |
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outputs = gr.outputs.Label(num_top_classes=3) |
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btn = gr.Button("Predict") |
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btn.click(fn=predict, inputs=inputs, outputs=outputs) |
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df=pd.read_excel("recommendation.xlsx") |
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classes = df['class'].unique() |
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with gr.Accordion("Find your skin condition using above analyzer and see the Recommended solutions",open=False): |
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for c in classes: |
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with gr.Accordion(c,open=False): |
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df_temp = df[df['class']==c] |
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for i,current_row in df_temp.iterrows(): |
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html_box = gr.HTML("<span><a href='{}'><img src ='{}'></a></span>".format(current_row['profit_link'],current_row['product_image'])) |
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demo.launch() |
