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| from fastai import * | |
| from fastcore.all import * | |
| from fastai.vision.all import * | |
| import pandas as pd | |
| import re | |
| import gradio as gr | |
| def get_x(data_set): | |
| return Path(data_set['image_path']) | |
| def get_y(data_set): | |
| return data_set['bf_est'] | |
| def new_splitter(df): | |
| # Get the unique values in the 'id' column | |
| unique_ids = df['id'].unique() | |
| # Shuffle the unique values | |
| np.random.seed(42) | |
| np.random.shuffle(unique_ids) | |
| # Calculate the number of unique values to be included in the first dataframe | |
| num_unique_in_test = int(np.ceil(len(unique_ids) * 0.8)) | |
| # Get the first 'num_unique_in_df1' unique values | |
| test_ids = unique_ids[:num_unique_in_test] | |
| # Get the rows of the original dataframe that contain the 'df1_ids' | |
| test = df.index[df['id'].isin(test_ids)].tolist() | |
| # Get the rest of the rows from the original dataframe | |
| valid = df.index[~df['id'].isin(test_ids)].tolist() | |
| return test, valid | |
| title = "Body Fat Predictor" | |
| description = "A Body Fat Predictor trained on the subreddit \"guessmybf\". \ | |
| For best preformence upload a front facing photo. \n The Range button allows \ | |
| you to adjust the range of the predicted body fat percentage. \ | |
| Choosing a higher range results in a less specific prediction but a more ecurate estimate, \ | |
| while a lower range provides a less precise estimate. \n The Examples section \ | |
| provides visual examples of different body fat percentages and how they appear \ | |
| on an individual's body. It is important to note that two individuals can have \ | |
| the same body fat percentage but look different due to differences in body composition \ | |
| (such as muscle mass)." | |
| learner = load_learner("bf_model.pkl") | |
| # def predict_bf(img): | |
| # return round(float(learner.predict(img)[1]),2) | |
| def predict_bf(img, range=0): | |
| if range not in [0,1,2,3,4]: | |
| range = 1 | |
| prediction = math.floor(float(learner.predict(img)[1])) | |
| if range ==0: | |
| return f"{str(prediction)}%" | |
| else: | |
| return f"{str(round(prediction) - range)}-{str(round(prediction) + range)}%" | |
| examples=[ | |
| ['/exp/6-8.jpg', '6-8'], | |
| ['/exp/7-9.jpg', '7-9'], | |
| ['/exp/9-11.jpg', '9-11'], | |
| ['/exp/9-11v2.jpg', '9-11'], | |
| ['/exp/10-12.jpg', '10-12'], | |
| ['/exp/10-12v2.jpg', '10-12'], | |
| ['/exp/11-13.jpg', '11-13'], | |
| ['/exp/13-15.jpg', '13-15'], | |
| ['/exp/13-15v2.jpg', '13-15'], | |
| ['/exp/15-17.jpg', '15-17'], | |
| ['/exp/15-17v2.jpg', '15-17'], | |
| ['/exp/15-17v3.jpg', '15-17'], | |
| ['/exp/17-19.jpg', '17-19'], | |
| ['/exp/19-20.jpg', '19-20'], | |
| ['/exp/19-20v2.jpg', '19-20'], | |
| ['/exp/21-23.jpg', '21-23'], | |
| ['/exp/22-24.jpg', '22-24'], | |
| ['/exp/23-25.jpg', '23-25'], | |
| ['/exp/24-26.jpg', '24-26'] | |
| ] | |
| image = gr.Image(shape=(192,192)) | |
| intf = gr.Interface(fn =predict_bf, inputs = [image, gr.Radio([0,1,2,3,4])], outputs = gr.outputs.Textbox(), | |
| title = title, description = description, examples=examples) | |
| intf.launch(inline= True) | |