Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| import numpy as np | |
| import tensorflow as tf | |
| def softmax(vector): | |
| e = np.exp(vector) | |
| return e / e.sum() | |
| def image_to_output (input_img): | |
| gr_img = [] | |
| gr_img.append(input_img) | |
| img2 = tf.image.resize(tf.cast(gr_img, tf.float32)/255. , [224, 224]) | |
| # print(img2) | |
| x_test = np.asarray(img2) | |
| prediction = model2.predict(x_test,batch_size=1).flatten() | |
| prediction = softmax(prediction) | |
| confidences = {labels[i]: float(prediction[i]) for i in range(102)} | |
| # confidences = {labels[i]:float(top[i]) for i in range(num_predictions)} | |
| return confidences | |
| # Download the model checkpoint | |
| import os | |
| import requests | |
| pretrained_repo = 'pretrained_model' | |
| model_repo_link = 'https://huggingface.co/qmjnh/flowerClassification_2/resolve/main/' | |
| for item in [ | |
| 'variables.data-00000-of-00001', | |
| 'variables.index', | |
| 'keras_metadata.pb', | |
| 'saved_model.pb', | |
| ]: | |
| params = requests.get(model_repo_link+item) | |
| if item.startswith('variables'): | |
| output_file = os.path.join(pretrained_repo, 'variables', item) | |
| else: | |
| output_file = os.path.join(pretrained_repo, item) | |
| if not os.path.exists(os.path.dirname(output_file)): | |
| os.makedirs(os.path.dirname(output_file)) | |
| with open(output_file, 'wb') as f: | |
| print(f'Downloading from {model_repo_link+item} to {output_file}') | |
| f.write(params.content) | |
| # Load the model | |
| model2=tf.keras.models.load_model(pretrained_repo) | |
| # Read the labels | |
| with open('flower_names.txt') as f: | |
| labels = f.readlines() | |
| # Run gradio | |
| from gradio.components import Image as gradio_image | |
| from gradio.components import Label as gradio_label | |
| UI=gr.Interface(fn=image_to_output, | |
| inputs=gradio_image(shape=(224,224)), | |
| outputs=gradio_label(num_top_classes=5), | |
| interpretation="default" | |
| ) | |
| description = "This model was trained to recognize 102 types of flowers. For the model to work with high accuracy, refer to the trained flowers [here](https://huggingface.co/spaces/qmjnh/flowerClassification_2/blob/main/flower_names.txt)" | |
| UI=gr.Interface(fn=image_to_output, | |
| inputs=gradio_image(shape=(224,224)), | |
| outputs=gradio_label(num_top_classes=5), | |
| interpretation="default", | |
| description=description, | |
| title="Flower Classifier", | |
| article="*built by qmjnh*" | |
| ) | |
| description = "This model was trained to recognize 102 types of flowers. For the model to work with high-accuracy, refer to the trained flowers [here](https://huggingface.co/spaces/qmjnh/flowerClassification_2/blob/main/flower_names.txt)" | |
| article1="This is an AI model trained to predict the name of the flower in the input picture. To try out the model, simply drop/upload a picture into the '*input box*' and press 'Submit'. The predictions will show up in the '*output box*'\n. Since the model was only trained to classify 102 types of flowers (flowers list can be found [here](https://huggingface.co/spaces/qmjnh/flowerClassification_2/blob/main/flower_names.txt) ), the prediction might be incorrect, but chances are if you try googling the names predicted by the model, the resulting flower will be very familiar to that in your picture. " | |
| article2="\n *built by qmjnh*" | |
| UI=gr.Interface(fn=image_to_output, | |
| inputs=gradio_image(shape=(224,224)), | |
| outputs=gradio_label(num_top_classes=5), | |
| interpretation="none", | |
| description=description, | |
| title="Flower Classifier", | |
| article= article1 + article2 | |
| ) | |
| UI.launch(share=True) |