| import streamlit as st | |
| import requests | |
| from io import BytesIO | |
| from PIL import Image | |
| import os | |
| api_key = os.environ['API_KEY'] | |
| API_URL = "/static-proxy?url=https%3A%2F%2Fapi-inference.huggingface.co%2Fmodels%2FHrishikesh332%2Fautotrain-meme-classification-42897109437%26quot%3B%3C%2Fspan%3E%3C!-- HTML_TAG_END --> | |
| headers = {"Authorization": f"Bearer {api_key}"} | |
| def query(data : bytes): | |
| response = requests.post(API_URL, headers=headers, data=data) | |
| return response.json() | |
| st.markdown("<h1 style='text-align: center;'>Memeter 💬</h1>", unsafe_allow_html=True) | |
| st.markdown("---") | |
| with st.sidebar: | |
| st.title("Memometer") | |
| st.caption(''' | |
| Memeter is an application used for the classification of whether the images provided is meme or not meme | |
| ''', unsafe_allow_html=False) | |
| img = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"]) | |
| if img is not None: | |
| data = img.read() | |
| output = query(data) | |
| st.write("Predicted Output:", output) |