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import streamlit as st
from transformers import pipeline
import requests
from PIL import Image
from io import BytesIO
import pandas as pd
st.subheader("Image Classification", divider='orange')
if st.toggle(label='Show Pipe4'):
models = [
'google/vit-base-patch16-224',
'WinKawaks/vit-tiny-patch16-224',
'microsoft/resnet-50',
'facebook/deit-base-distilled-patch16-224',
'facebook/convnext-large-224',
'apple/mobilevit-small',
]
model_name = st.selectbox(
label='Select Model',
options=models,
placeholder='google/vit-base-patch16-224',
)
pipe = pipeline("image-classification", model=model_name)
url = 'https://media.istockphoto.com/id/182756302/photo/hot-dog-with-grilled-peppers.jpg?s=1024x1024&w=is&k=20&c=NCHo2P94a-PfRDKzWSe4h6oACQZ-_ubZqUBj5CMSEWY='
response = requests.get(url=url)
image_bytes = BytesIO(response.content)
image = Image.open(image_bytes)
# image = Image.open(BytesIO(requests.get(url).content))
# use_default = st.checkbox(label='Use default image')
file = st.file_uploader(label='Upload image')
if file is not None:
image = Image.open(file)
res = pipe(image)
if st.toggle(label='Show row data'):
st.write(res)
p = pd.DataFrame(res)
p = p.sort_values(by='score',ascending=False)
col1, col2 = st.columns(2)
col1.write(image)
col2.write(p['label'])
st.bar_chart(p.set_index('label'))
st.area_chart(p.set_index('label'))
# col2.bar_chart(p.set_index('label'))
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