| import streamlit as st | |
| from PIL import Image | |
| import replicate | |
| import ast | |
| import requests | |
| import io | |
| API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image" | |
| def query(payload): | |
| response = requests.post(API_URL, json=payload) | |
| return response.content | |
| st.header("Mirror: AI Stylist") | |
| uploaded_file = st.file_uploader("Upload Your Photo", type=["jpg", "jpeg", "png"]) | |
| image = "" | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption="Uploaded Image.", use_column_width=True) | |
| st.markdown("Generating Fashion Accessories For You...") | |
| with st.spinner("Loading..."): | |
| prompt = ''' | |
| You are a personal stylist recommending fashion advice and clothing combinations. | |
| Use the person's dressing style, colour of clothes and appearance from the given | |
| image to generate three fashion accessories according to the gender of the person | |
| in an array format. | |
| Examples: | |
| Example 1: ["accessory1 name with speicific detail"] | |
| ''' | |
| input = { | |
| "image": uploaded_file, | |
| "prompt": prompt | |
| } | |
| output = replicate.run( | |
| "yorickvp/llava-13b:b5f6212d032508382d61ff00469ddda3e32fd8a0e75dc39d8a4191bb742157fb", | |
| input=input, | |
| ) | |
| arr = "".join(output) | |
| output = ast.literal_eval(arr) | |
| for item in output: | |
| image_bytes = query({ | |
| "inputs": item, | |
| }) | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| st.image(image, caption=item, use_column_width=True) | |
| st.success("Generated Fashion Accessories!") | |