# import streamlit as st
# from transformers import pipeline

# # Initialize the image captioning pipeline
# captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")

# # Streamlit app title
# st.title("Image to Text Captioning")

# # Input for image URL
# image_url = st.text_input("Enter the URL of the image:")

# # If an image URL is provided
# if image_url:
#     try:
#         # Display the image
#         st.image(image_url, caption="Provided Image", use_column_width=True)
        
#         # Generate the caption
#         caption = captioner(image_url)
        
#         # Display the caption
#         st.write("**Generated Caption:**")
#         st.write(caption[0]['generated_text'])
#     except Exception as e:
#         st.error(f"An error occurred: {e}")

# # To run this app, save this code to a file (e.g., `app.py`) and run `streamlit run app.py` in your terminal.


import streamlit as st
import torch
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import nltk

nltk.download('punkt')

@st.cache_resource
def load_models():
    processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
    model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
    tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-base-tag-generation")
    model2 = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-base-tag-generation")
    return processor, model, tokenizer, model2

processor, model, tokenizer, model2 = load_models()

def get_image_caption_and_tags(img_url):
    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

    # conditional image captioning
    alltexts = "a photography of"
    inputs = processor(raw_image, alltexts, return_tensors="pt")
    out = model.generate(**inputs)
    conditional_caption = processor.decode(out[0], skip_special_tokens=True)

    # unconditional image captioning
    inputs = processor(raw_image, return_tensors="pt")
    out = model.generate(**inputs)
    unconditional_caption = processor.decode(out[0], skip_special_tokens=True)

    inputs = tokenizer([alltexts], max_length=512, truncation=True, return_tensors="pt")
    output = model2.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)
    decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
    tags = list(set(decoded_output.strip().split(", ")))

    return raw_image, conditional_caption, unconditional_caption, tags

st.title('Image Captioning and Tag Generation')

img_url = st.text_input("Enter Image URL:")

if st.button("Generate Captions and Tags"):
    with st.spinner('Processing...'):
        try:
            image, cond_caption, uncond_caption, tags = get_image_caption_and_tags(img_url)
            st.image(image, caption='Input Image', use_column_width=True)
            st.subheader("Conditional Caption:")
            st.write(cond_caption)
            st.subheader("Unconditional Caption:")
            st.write(uncond_caption)
            st.subheader("Generated Tags:")
            st.write(", ".join(tags))
        except Exception as e:
            st.error(f"An error occurred: {e}")