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import streamlit as st | |
import requests | |
import io | |
from PIL import Image | |
import os | |
hf_token = os.environ.get("hf_token") | |
API_URL_KVI = "https://api-inference.huggingface.co/models/Kvikontent/kviimager2.0" | |
API_URL_MJ = "https://api-inference.huggingface.co/models/Kvikontent/midjourney-v6" | |
API_URL_DALLE = "https://api-inference.huggingface.co/models/ehristoforu/dalle-3-xl" | |
headers = {"Authorization": f"Bearer {hf_token}"} | |
def query(payload): | |
response = requests.post(API_URL, headers=headers, json=payload) | |
return response.content | |
st.title("Text To Image models") | |
st.write("Choose model and enter a prompt") | |
model = st.selectbox( | |
"Choose model", | |
("KVIImager2.0", "Midjourney V6", "Dalle 3") | |
) | |
prompt = st.text_input("Enter prompt") | |
if prompt: | |
if model == "KVIImager2.0": | |
API_URL = API_URL_KVI | |
elif model == "Midjourney V6": | |
API_URL = API_URL_MJ | |
elif model == "Dalle 3": | |
API_URL = API_URL_DALLE | |
image_bytes = query({ | |
"inputs": prompt, | |
}) | |
st.image(image_bytes, caption="Generated Image") | |
st.info("Image generated successfully!") |