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| import streamlit as st | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM | |
| import nltk | |
| from youtube_transcript_api import YouTubeTranscriptApi | |
| # 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") | |
| # Function to fetch YouTube transcript | |
| def fetch_transcript(url): | |
| video_id = url.split('watch?v=')[-1] | |
| try: | |
| transcript = YouTubeTranscriptApi.get_transcript(video_id) | |
| transcript_text = ' '.join([entry['text'] for entry in transcript]) | |
| return transcript_text | |
| except Exception as e: | |
| return str(e) | |
| # Streamlit app title | |
| st.title("Multi-purpose Machine Learning App") | |
| # Create tabs for different functionalities | |
| tab1, tab2, tab3 = st.tabs(["Image Captioning", "Text Tag Generation", "YouTube Transcript"]) | |
| # 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.") | |
| # YouTube Transcript Tab | |
| with tab3: | |
| st.header("YouTube Video Transcript Extractor") | |
| # Input for YouTube URL | |
| youtube_url = st.text_input("Enter YouTube URL:") | |
| # Button to get transcript | |
| if st.button("Get Transcript"): | |
| if youtube_url: | |
| transcript = fetch_transcript(youtube_url) | |
| if "error" not in transcript.lower(): | |
| st.success("Transcript successfully fetched!") | |
| st.text_area("Transcript", transcript, height=300) | |
| else: | |
| st.error(f"An error occurred: {transcript}") | |
| else: | |
| st.warning("Please enter a URL.") | |