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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, T5ForConditionalGeneration, T5Tokenizer | |
| import nltk | |
| import torch | |
| from textblob import TextBlob | |
| from nltk.corpus import stopwords | |
| from nltk.tokenize import word_tokenize | |
| nltk.download('punkt') | |
| nltk.download('averaged_perceptron_tagger') | |
| nltk.download('stopwords') | |
| # Load models and tokenizers | |
| tag_tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-base-tag-generation") | |
| tag_model = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-base-tag-generation") | |
| 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) | |
| # Function to generate tags | |
| def generate_tags(text): | |
| with torch.no_grad(): | |
| inputs = tag_tokenizer(text, max_length=256, truncation=True, return_tensors="pt") | |
| output = tag_model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64, num_return_sequences=1) | |
| decoded_output = tag_tokenizer.batch_decode(output, skip_special_tokens=True)[0] | |
| tags = list(set(decoded_output.strip().split(", "))) | |
| return tags | |
| # Function to generate summaries | |
| def generate_summary(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 extract keywords and generate hashtags | |
| def extract_keywords(content): | |
| text = content.lower() | |
| sentences = nltk.sent_tokenize(text) | |
| keywords = [] | |
| for sentence in sentences: | |
| words = nltk.word_tokenize(sentence) | |
| tags = nltk.pos_tag(words) | |
| for word, tag in tags: | |
| if tag.startswith('NN'): | |
| keywords.append(word) | |
| return keywords | |
| def generate_hashtags(content, max_hashtags=10): | |
| keywords = extract_keywords(content) | |
| hashtags = [] | |
| for keyword in keywords: | |
| hashtag = "#" + keyword | |
| if len(hashtag) <= 20: | |
| hashtags.append(hashtag) | |
| return hashtags[:max_hashtags] | |
| # Function to extract point of view | |
| def extract_point_of_view(text): | |
| stop_words = set(stopwords.words('english')) | |
| words = word_tokenize(str(text)) | |
| filtered_words = [word for word in words if word.casefold() not in stop_words] | |
| text = ' '.join(filtered_words) | |
| blob = TextBlob(text) | |
| polarity = blob.sentiment.polarity | |
| subjectivity = blob.sentiment.subjectivity | |
| if polarity > 0.5: | |
| point_of_view = "Positive" | |
| elif polarity < -0.5: | |
| point_of_view = "Negative" | |
| else: | |
| point_of_view = "Neutral" | |
| return point_of_view | |
| # Streamlit application | |
| st.title("LinkedIn Post Analysis AI") | |
| text = st.text_area("Enter the LinkedIn Post:") | |
| if st.button("Analyze:"): | |
| if text: | |
| # Generate tags | |
| tags = generate_tags(text) | |
| st.subheader("The Most Tracked KeyWords:") | |
| st.write(tags) | |
| # Generate summaries | |
| summary1 = generate_summary(text, 'summary: ') | |
| summary2 = generate_summary(text, 'summary brief: ') | |
| st.subheader("Summary Title 1:") | |
| st.write(summary1) | |
| st.subheader("Summary Title 2:") | |
| st.write(summary2) | |
| # Generate hashtags | |
| hashtags = generate_hashtags(text) | |
| st.subheader("Generated Hashtags for the Post") | |
| st.write(hashtags) | |
| # Extract point of view | |
| point_of_view = extract_point_of_view(text) | |
| st.subheader("Tone of the Post:") | |
| st.write(point_of_view) | |
| else: | |
| st.warning("Please enter text to analyze.") | |