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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) |