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import gradio as gr |
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import tensorflow as tf |
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import numpy as np |
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import os |
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import pandas as pd |
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model_gender = tf.keras.models.load_model('model_gender.h5') |
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model_age = tf.keras.models.load_model('model_age.h5') |
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actual_data = { |
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"000000.png": {"Image": 1,"age": 61.0, "gender": "Female"}, |
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"000001.png": {"Image": 2,"age": 63.0, "gender": "Male"}, |
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"000002.png": {"Image": 3,"age": 45.0, "gender": "Male"}, |
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"000003.png": {"Image": 4,"age": 59.0, "gender": "Female"}, |
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"000004.png": {"Image": 5,"age": 37.0, "gender": "Female"} |
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} |
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df = pd.DataFrame(actual_data).T |
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data = {'Name': ['Accuracy', 'Precision', 'Recall', 'F1-score'], |
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'Value': [96.11 , 0.9368, 0.9731, 0.9546]} |
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df1 = pd.DataFrame(data) |
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def preprocess_image(image): |
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img = image.convert('L') |
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img = img.resize((128, 128)) |
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img = np.array(img) / 255.0 |
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img = img.reshape((1, 128, 128, 1)) |
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return img |
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def predict(image): |
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preprocessed_image = preprocess_image(image) |
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gender_pred = model_gender.predict(preprocessed_image)[0][0] |
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age_pred = model_age.predict(preprocessed_image)[0][0] |
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gender = "Male" if gender_pred > 0.68 else "Female" |
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list = "{:.2f}".format(age_pred),gender,df |
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return list |
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text_age = gr.components.Textbox(label="Predicted Age") |
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text_gender = gr.components.Textbox(label="Predicted Gender") |
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def predictor_tab(): |
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interface = gr.Interface(predict, gr.components.Image(height=440,width=1000,label="Upload Image", type="pil"), |
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outputs=[text_age, text_gender, gr.DataFrame(value=df)], |
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examples=[ |
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os.path.join(os.path.dirname(__file__),"00000.png"), |
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os.path.join(os.path.dirname(__file__),"00001.png"), |
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os.path.join(os.path.dirname(__file__),"00002.png"), |
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os.path.join(os.path.dirname(__file__),"00003.png"), |
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os.path.join(os.path.dirname(__file__),"00004.png")], |
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allow_flagging='never') |
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return interface |
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def about_tab(): |
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with gr.Blocks() as about: |
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gr.Markdown("# Age and Gender Prediction with Deep Learning!") |
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gr.Markdown("This awesome app uses deep learning magic ✨ to predict someone's age and gender based on a x-ray image! Just upload a photo, and our clever models will do their best detective work to unveil the mystery.") |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("**DATASET 🗃**") |
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gr.Markdown( |
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""" |
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The lung scans used in this project come from a publicly available dataset. |
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It contains approximately 10,700 scans for training and 11,700 scans for testing. |
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This dataset was part of a competition held by The Radiology and Diagnostic Imaging Society of São Paulo (SPR) with Amazon Web Services. |
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""" |
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) |
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gr.Text("https://www.kaggle.com/datasets/felipekitamura/spr-x-ray-age-and-gender-dataset",label="link") |
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gr.Markdown("**Model Performance 💡**") |
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table = gr.DataFrame(value=df1) |
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gr.Markdown("⚜ **Model Accuracy for Genders**") |
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gender_img = 'gender.png' |
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gr.Image(value=gender_img, width=500, height=450) |
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gr.Markdown("⚜ **Model Accuracy for Age**") |
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age_img = 'age.png' |
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gr.Image(value=age_img, width=500, height=450) |
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gr.Markdown("⚜ **Confusion matrix**") |
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matrix_img = 'matrix.png' |
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gr.Image(value=matrix_img, width=500, height=450) |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("**Created by 🤓**") |
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gr.Markdown("Uday Jawheri") |
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with gr.Row(): |
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gr.Text("https://www.linkedin.com/in/uday-jawheri/", label="LinkedIn") |
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gr.Text("https://xudayx.github.io/Portfolio/", label="Website") |
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return about |
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with gr.Blocks(theme=gr.themes.Soft(), title="Age and Gender Prediction") as app: |
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with gr.Tab("Predictor"): |
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predictor_tab() |
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with gr.Tab("About"): |
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about_tab() |
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app.launch() |
