gradio/demo/image_classification/run.py
Ali Abdalla 597337dcb8
Adding a Playground Tab to the Website (#1860)
* added playground with 12 demos

* change name to recipes, restyle navbar

* add explanatory text to page

* fix demo mapping

* categorize demos, clean up design

* styling

* cateogry naming and emojis

* refactor and add text demos

* add view code button

* remove opening slash in embed

* styling

* add image demos

* adding plot demos

* remove see code button

* removed submodules

* changes

* add audio models

* remove fun section

* remove tests in image semgentation demo repo

* requested changes

* add outbreak_forecast

* fix broken demos

* remove images and models, add new demos

* remove readmes, change to run.py, add description as comment

* move to /demos folder, clean up dict

* add upload_to_spaces script

* fix script, clean repos, and add to docker file

* fix python versioning issue

* env variable

* fix

* env fixes

* spaces instead of tabs

* revert to original networking.py

* fix rate limiting in asr and autocomplete

* change name to demos

* clean up navbar

* move url and description, remove code comments

* add tabs to demos

* remove margins and footer from embedded demo

* font consistency

Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
2022-09-15 08:24:10 -07:00

23 lines
732 B
Python

import gradio as gr
import torch
import requests
from torchvision import transforms
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def predict(inp):
inp = transforms.ToTensor()(inp).unsqueeze(0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
return confidences
demo = gr.Interface(fn=predict,
inputs=gr.inputs.Image(type="pil"),
outputs=gr.outputs.Label(num_top_classes=3),
examples=[["cheetah.jpg"]],
)
demo.launch()