| #import gradio as gr | |
| #def greet(name): | |
| # return "Hello " + name + "!!" | |
| # | |
| #demo = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| #demo.launch() | |
| # second time used this only but huggingface doesn't like it so moving to a different method | |
| #learner=load_learner('model.pkl') | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| def is_cat(x): return x[0].isupper() | |
| dls = ImageDataLoaders.from_lists('.', fnames=['cat.jpeg','dog.jpeg'], labels=['cat','dog'], vocab=['cat', 'dog']) | |
| learner = vision_learner(dls, resnet18, metrics=error_rate) | |
| learner.load('model') | |
| categories = ['Dog','Cat'] | |
| def classify_image(img): | |
| pred,pred_idx,probs = learner.predict(img) | |
| return dict(zip(categories, map(float,probs))) | |
| image=gr.Image() | |
| #image=gr.Image(shape=(192,192)) | |
| label=gr.Label() | |
| examples=['dog.jpeg', 'cat.jpeg','dogcat.jpeg'] | |
| titleText="Dog vs Cat Classifier" | |
| descriptionText="Upload an image of a dog or a cat to predict the probabilities of each class." | |
| intf=gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples, | |
| title=titleText, description=descriptionText) | |
| intf.launch() |