Spaces:
Sleeping
Sleeping
File size: 1,209 Bytes
7d98a96 bc2a9ae 7d98a96 3e53610 8b4348f 3e53610 bc2a9ae 8b4348f 7d98a96 a90577f 7d98a96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 |
import gradio as gr
from fastai.vision.all import *
import skimage
import re
def from_csv(x):
try:
pattern = '\/([A-Za-z\d]+.jpg)'
match = re.findall(pattern,str(x))
# print(match)
x = df.loc[df['image'] == match[0]]
# print(x)
y = x['emotion'].item() # #y=x['label'].values[0]
return str(y)
except:
# print('check these files')
# print(x)
return 0
learn = load_learner('export.pkl')
labels = learn.dls.vocab
def predict(img):
img = PILImage.create(img)
pred,pred_idx,probs = learn.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}
title = "EMOTIONAL DAMAGE"
description = "Find your true emotions"
article="<p style='text-align: center'><a href='https://www.linkedin.com/in/ranjith-azad-506201238/' target='_blank'>Linkedin</a></p>"
examples = ['ha.jpg','damu.jpeg','kili.jpg','sad.jpg','fear.jpg','disg.jpg','anger.jpg']
interpretation='default'
enable_queue=True
gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=5),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
|