start

app.py

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+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.