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import streamlit as st
import json
import requests
import base64
from PIL import Image
import io
def get_prediction(image_data):
#replace your image classification ai service URL
url = 'https://askai.aiclub.world/9e64ab8b-95e4-40fa-9529-b13d9e1b4761'
r = requests.post(url, data=image_data)
st.write(r)
response = r.json()['predicted_label']
score = r.json()['score']
#print("Predicted_label: {} and confidence_score: {}".format(response,score))
return response, score
#creating the web app
#setting up the title
st.title("Cats and Dogs Image Classifier")#change according to your project
#setting up the subheader
st.subheader("File Uploader")#change according to your project
#file uploader
image = st.file_uploader(label="Upload an image",accept_multiple_files=False, help="Upload an image to classify them")
if image:
#converting the image to bytes
img = Image.open(image)
buf = io.BytesIO()
img.save(buf,format = 'JPEG')
byte_im = buf.getvalue()
#converting bytes to b64encoding
payload = base64.b64encode(byte_im)
#file details
file_details = {
"file name": image.name,
"file type": image.type,
"file size": image.size
}
#write file details
st.write(file_details)
#setting up the image
st.image(img)
#predictions
response, scores = get_prediction(payload)
col1, col2 = st.columns(2)
with col1:
st.metric("Prediction Label",response)
with col2:
st.metric("Confidence Score", max(scores))
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