Spaces:
Running
Running
File size: 1,422 Bytes
1e1a418 96072ff 37bed6c 1e1a418 6d07f80 37bed6c 63b4a09 37bed6c 2349882 6d07f80 2349882 6d07f80 37bed6c 6d07f80 37bed6c 2349882 37bed6c 6d07f80 37bed6c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
import torch
import re
from PIL import Image
import requests
import streamlit as st
from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
from PIL import Image
import requests
from langchain.indexes import VectorstoreIndexCreator
from langchain.document_loaders import ImageCaptionLoader
st.set_page_config(page_title="Captionize")
st.title("🤖 Captionize")
st.subheader("Generate Captions for your Image...")
st.sidebar.image('./csv_analysis.png',width=300, use_column_width=True)
# Applying Styling
st.markdown("""
<style>
div.stButton > button:first-child {
background-color: #0099ff;
color:#ffffff;
}
div.stButton > button:hover {
background-color: #00ff00;
color:#FFFFFF;
}
</style>""", unsafe_allow_html=True)
#pic = st.file_uploader(label="Please upload any Image here 😎",type=['png', 'jpeg', 'jpg'], help="Only 'png', 'jpeg' or 'jpg' formats allowed")
examples = [f"example{i}.jpg" for i in range(1,7)]
#Image.open(requests.get(pic, stream=True).raw).convert("RGB")
loader = ImageCaptionLoader(path_images=examples)
list_docs = loader.load()
index = VectorstoreIndexCreator().from_loaders([loader])
button = st.button("Generate Caption")
query = st.text_area("Enter your query 🔍")
if button:
Image.open(requests.get(examples[0], stream=True).raw).convert("RGB")
# Get Response
caption = index.query(query)
st.write(caption) |