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Browse files
app.py
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import streamlit as st
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from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
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import nltk
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# Download NLTK data
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nltk.download('punkt')
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# Initialize the image captioning pipeline
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captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
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# Load the tokenizer and model for tag generation
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tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-base-tag-generation")
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model = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-base-tag-generation")
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# Streamlit app title
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st.title("Multi-purpose Machine Learning App")
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# Create tabs for different functionalities
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tab1, tab2 = st.tabs(["Image Captioning", "Text Tag Generation"])
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# Image Captioning Tab
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with tab1:
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st.header("Image Captioning")
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# Input for image URL
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image_url = st.text_input("Enter the URL of the image:")
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# If an image URL is provided
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if image_url:
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try:
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# Display the image
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st.image(image_url, caption="Provided Image", use_column_width=True)
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# Generate the caption
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caption = captioner(image_url)
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# Display the caption
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st.write("**Generated Caption:**")
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st.write(caption[0]['generated_text'])
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except Exception as e:
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st.error(f"An error occurred: {e}")
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# Text Tag Generation Tab
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with tab2:
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st.header("Text Tag Generation")
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# Text area for user input
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text = st.text_area("Enter the text for tag extraction:", height=200)
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# Button to generate tags
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if st.button("Generate Tags"):
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if text:
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try:
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# Tokenize and encode the input text
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inputs = tokenizer([text], max_length=512, truncation=True, return_tensors="pt")
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# Generate tags
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output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)
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# Decode the output
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decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
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# Extract unique tags
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tags = list(set(decoded_output.strip().split(", ")))
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# Display the tags
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st.write("**Generated Tags:**")
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st.write(tags)
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except Exception as e:
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st.error(f"An error occurred: {e}")
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else:
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st.warning("Please enter some text to generate tags.")
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# To run this app, save this code to a file (e.g., `app.py`) and run `streamlit run app.py` in your terminal.
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