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import streamlit as st | |
import requests | |
from io import BytesIO | |
from PIL import Image | |
import os | |
api_key = os.environ['API_KEY'] | |
API_URL = "https://api-inference.huggingface.co/models/Hrishikesh332/autotrain-meme-classification-42897109437" | |
headers = {"Authorization": f"Bearer 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"} | |
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("Memeter") | |
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) | |