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import streamlit as st
from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO
$ pip install streamlit --upgrade
hf_token = str(st.secrets["HF_TOKEN"])
# Title
st.title("Image Classification Web App")
st.markdown("This app uses Hugging Face's 'transformers' library to classify images using pre-trained models. The app uses three different models for image classification: swin, convnext and vit. Please select a model to classify the image you put on the left sidebar.")
# Intro
st.sidebar.markdown("**Please provide a Satellite image for classification**")
# Image input via URL
url = st.sidebar.text_input("Image URL")
if url:
try:
response = requests.get(url)
image = Image.open(BytesIO(response.content))
st.sidebar.image(image, caption='Uploaded Image', use_column_width=True)
except Exception as e:
st.sidebar.error("Invalid URL. Please enter a valid URL for an image.")
# Image input via file uploader on the sidebar (but display image on the main page)
uploaded_file = st.sidebar.file_uploader("Or upload an image", type=["jpg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
# Documentation about the 3 models
st.sidebar.markdown("## Find more information about the model architecture at the link below : ")
st.sidebar.markdown("*Vision Transformer (ViT)* https://huggingface.co/docs/transformers/main/en/model_doc/vit")
st.sidebar.markdown("*ConvNext Transformer* https://huggingface.co/docs/transformers/main/en/model_doc/convnext")
st.sidebar.markdown("*Swin Transformer* https://huggingface.co/docs/transformers/main/en/model_doc/swin")
# Image classification function
def classify_image1(image):
pipe1 = pipeline("image-classification", "SolubleFish/swin_transformer-finetuned-eurosat", token=hf_token)
return pipe1(image)
def classify_image2(image):
pipe2 = pipeline("image-classification", "SolubleFish/image_classification_convnext", token=hf_token)
return pipe2(image)
def classify_image3(image):
pipe3 = pipeline("image-classification", "SolubleFish/image_classification_vit", token=hf_token)
return pipe3(image)
# Create three columns
col1, col2, col3 = st.columns(3)
# Classification button for classify_image1
if col1.button("Classify Image by Swin"):
if url or uploaded_file:
results = classify_image1(image)
if results:
# Use markdown to present the results
for result in results:
col1.markdown(f"Class name: **{result['label']}** \n\n Confidence: **{str(format(result['score']*100, '.2f'))}**"+"%")
col1.success("Classification completed.")
else:
col1.error("No results found.")
else:
col1.error("Please provide an image for classification.")
# Classification button for classify_image2
if col2.button("Classify Image by ConvNext"):
if url or uploaded_file:
results = classify_image2(image)
if results:
# Use markdown to present the results
for result in results:
col2.markdown(f"Class name: **{result['label']}** \n\n Confidence: **{str(format(result['score']*100, '.2f'))}**"+"%")
col2.success("Classification completed.")
else:
col2.error("No results found.")
else:
col2.error("Please provide an image for classification.")
# Classification button for classify_image3
if col3.button("Classify Image by ViT"):
if url or uploaded_file:
results = classify_image3(image)
if results:
# Use markdown to present the results
for result in results:
col3.markdown(f"Class name: **{result['label']}** \n\n Confidence: **{str(format(result['score']*100, '.2f'))}**"+"%")
col3.success("Classification completed.")
else:
col3.error("No results found.")
else:
col3.error("Please provide an image for classification.") |