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| import streamlit as st | |
| from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline, AutoTokenizer, AutoModelForSeq2SeqLM | |
| import nltk | |
| from youtube_transcript_api import YouTubeTranscriptApi | |
| # Download NLTK data | |
| nltk.download('punkt') | |
| # Load models and tokenizers | |
| summary_model_name = 'utrobinmv/t5_summary_en_ru_zh_base_2048' | |
| summary_model = T5ForConditionalGeneration.from_pretrained(summary_model_name) | |
| summary_tokenizer = T5Tokenizer.from_pretrained(summary_model_name) | |
| tag_tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-base-tag-generation") | |
| tag_model = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-base-tag-generation") | |
| captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base") | |
| # Function to summarize text | |
| def summarize_text(text, prefix): | |
| src_text = prefix + text | |
| input_ids = summary_tokenizer(src_text, return_tensors="pt") | |
| generated_tokens = summary_model.generate(**input_ids) | |
| result = summary_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) | |
| return result[0] | |
| # 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: WAVE_AI") | |
| # Create tabs for different functionalities | |
| tab1, tab2, tab3, tab4 = st.tabs(["Text Summarization", "Text Tag Generation", "Image Captioning", "YouTube Transcript"]) | |
| # Text Summarization Tab | |
| with tab1: | |
| st.header("Text Summarization") | |
| input_text = st.text_area("Enter the text to summarize:", height=300) | |
| if st.button("Generate Summaries"): | |
| if input_text: | |
| title1 = summarize_text(input_text, 'summary: ') | |
| title2 = summarize_text(input_text, 'summary brief: ') | |
| st.write("### Title 1") | |
| st.write(title1) | |
| st.write("### Title 2") | |
| st.write(title2) | |
| else: | |
| st.warning("Please enter some text to summarize.") | |
| # Text Tag Generation Tab | |
| with tab2: | |
| st.header("Text Tag Generation") | |
| text = st.text_area("Enter the text for tag extraction:", height=200) | |
| if st.button("Generate Tags"): | |
| if text: | |
| try: | |
| inputs = tag_tokenizer([text], max_length=512, truncation=True, return_tensors="pt") | |
| output = tag_model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64) | |
| decoded_output = tag_tokenizer.batch_decode(output, skip_special_tokens=True)[0] | |
| tags = list(set(decoded_output.strip().split(", "))) | |
| 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.") | |
| # Image Captioning Tab | |
| with tab3: | |
| st.header("Image Captioning Extractor") | |
| image_url = st.text_input("Enter the URL of the image:") | |
| if image_url: | |
| try: | |
| st.image(image_url, caption="Provided Image", use_column_width=True) | |
| caption = captioner(image_url) | |
| st.write("**Generated Caption:**") | |
| st.write(caption[0]['generated_text']) | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| # YouTube Transcript Tab | |
| with tab4: | |
| st.header("YouTube Video Transcript Extractor") | |
| youtube_url = st.text_input("Enter YouTube URL:") | |
| 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.") | |