streamlit_app.py

import json
import base64
import streamlit as st
import requests
from PIL import Image


page_title = "Serverless"
page_icon = "💛"
layout = "centered"
st.set_page_config(page_title=page_title, page_icon=page_icon, layout=layout)

# --- HIDE STREAMLIT STYLE ---
streamlit_style = """
			<style>
            footer {visibility: hidden;}
			@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@100&display=swap');
			html, body, [class*="css"]  {
			font-family: 'Roboto', sans-serif;
			}
			</style>
			"""
st.markdown(streamlit_style, unsafe_allow_html=True)

if __name__ == '__main__':

    st.title('Intel Scene Classification on :blue[Serverless]')
    instructions = """ To test, select an image from [here](https://github.com/Project-Kidu/code-repo/tree/main/tests/resources/intel-scene) """
    st.write(instructions)
    st.sidebar.markdown("Made with ☕ and ❤️ by [Gokul](https://github.com/gokul-pv)", unsafe_allow_html=True)

    file = st.file_uploader('Upload an Image', type= ['png', 'jpg', 'jpeg'])


    if file:  # if user uploaded file
        img = Image.open(file)
        st.subheader("Here is the image you've selected 🖼️")
        resized_image = img.resize((224, 224))
        st.image(resized_image)
        st.subheader("Here are the predictions 🧠")

        url = "https://5hbrm6xu63.execute-api.ap-south-1.amazonaws.com"
        ext = file.name.split('.')[-1]
        prefix = f'data:image/{ext};base64,'

        bytes_data = file.getvalue()
        
        base64_data = prefix + base64.b64encode(bytes_data).decode('utf-8')

        payload = json.dumps({"body": [base64_data]})
        headers = {"content-type": "application/json"}
        response = requests.request("POST", url, json=payload, headers=headers)

        st.json(response.json())