app.py

import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
import nltk

# Download NLTK data
nltk.download('punkt')

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

# Load the tokenizer and model for tag generation
tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-base-tag-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-base-tag-generation")

# Streamlit app title
st.title("Multi-purpose Machine Learning App")

# Create tabs for different functionalities
tab1, tab2 = st.tabs(["Image Captioning", "Text Tag Generation"])

# Image Captioning Tab
with tab1:
    st.header("Image 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}")

# Text Tag Generation Tab
with tab2:
    st.header("Text Tag Generation")
    
    # Text area for user input
    text = st.text_area("Enter the text for tag extraction:", height=200)
    
    # Button to generate tags
    if st.button("Generate Tags"):
        if text:
            try:
                # Tokenize and encode the input text
                inputs = tokenizer([text], max_length=512, truncation=True, return_tensors="pt")
                
                # Generate tags
                output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)
                
                # Decode the output
                decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
                
                # Extract unique tags
                tags = list(set(decoded_output.strip().split(", ")))
                
                # Display the tags
                st.write("**Generated Tags:**")
                st.write(tags)
            except Exception as e:
                st.error(f"An error occurred: {e}")
        else:
            st.warning("Please enter some text to generate tags.")

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