Spaces:
Runtime error
Runtime error
| ### ------------------------------- ### | |
| ### libraries ### | |
| ### ------------------------------- ### | |
| from tensorflow.keras.models import load_model | |
| import gradio as gr # remove later | |
| import numpy as np | |
| import os | |
| from yattag import Doc | |
| # import h5py # remove later | |
| ### -------------------------------- ### | |
| ### model loading ### | |
| ### -------------------------------- ### | |
| model = load_model('model.h5') # single file model from colab | |
| labels = ['please upload categories.txt' for i in range(10)] # placeholder | |
| ## --------------------------------- ### | |
| ### reading: categories.txt ### | |
| ### -------------------------------- ### | |
| if os.path.isfile("categories.txt"): | |
| # open info.txt in read mode | |
| categories = open("categories.txt", "r") | |
| labels = categories.readline().split() | |
| print(labels) | |
| ## --------------------------------- ### | |
| ### reading: info.txt ### | |
| ### -------------------------------- ### | |
| # placeholders in case info.txt does not exist | |
| placeholder = "please create an info.txt to customize this text" | |
| title = bkgd = data_collection = priv_cons = bias_cons = ident_cons = img_src = membs = placeholder | |
| description = "An AI project created by [name], [name], and [name]" | |
| # check if info.txt is present | |
| if os.path.isfile("info.txt"): | |
| # open info.txt in read mode | |
| info = open("info.txt", "r") | |
| # each line to a string | |
| title = info.readline() | |
| bkgd = info.readline() | |
| data_collection = info.readline() | |
| priv_cons = info.readline() | |
| bias_cons = info.readline() | |
| ident_cons = info.readline() | |
| img_src = info.readline() | |
| membs = info.readline() | |
| # close file | |
| info.close() | |
| # use yattag library to generate html | |
| doc, tag, text, line = Doc().ttl() | |
| # create html based on info.txt | |
| with tag('div'): | |
| with tag('div', klass='my-div'): | |
| line('h2', 'Project Background') | |
| line('p', bkgd) | |
| with tag('div', klass='my-div'): | |
| line('h2', 'Data Collection') | |
| line('p', data_collection) | |
| with tag('div', klass='my-div'): | |
| line('h2', 'Ethical Considerations') | |
| with tag('ul'): | |
| line('li', priv_cons) | |
| line('li', bias_cons) | |
| line('li', ident_cons) | |
| with tag('div', klass='my-div'): | |
| line('h2', 'Our Team') | |
| line('p', membs) | |
| doc.stag('img', src=img_src) | |
| my_css = ''' | |
| .my-div { | |
| border: 2px solid black; | |
| text-align: center; | |
| margin: 10px; | |
| padding: 5%; | |
| } | |
| ul { | |
| display: inline-block; | |
| text-align: left; | |
| } | |
| img { | |
| display: block; | |
| margin: auto; | |
| } | |
| .description { | |
| text-align: center; | |
| } | |
| ''' | |
| ### ------------------------------- ### | |
| ### interface creation ### | |
| ### ------------------------------- ### | |
| def preprocess(image): | |
| image = np.array(image) / 255 | |
| image = np.expand_dims(image, axis=0) | |
| return image | |
| def predict_image(image): | |
| pred = model.predict(preprocess(image)) | |
| results = {} | |
| for row in pred: | |
| for idx, item in enumerate(row): | |
| results[labels[idx]] = float(item) | |
| return results | |
| image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here") | |
| label = gr.outputs.Label(num_top_classes=len(labels)) | |
| gr.Interface(fn=predict_image, inputs=image, outputs=label, capture_session=True, article=doc.getvalue(), css=my_css, theme='huggingface', title=title, allow_flagging=False, description=description).launch(debug=True) |