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| import streamlit as st | |
| from transformers import pipeline, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM, AutoTokenizer | |
| class CombinedModel: | |
| def __init__(self, classifier_model, classifier_tokenizer, summarizer_model, summarizer_tokenizer): | |
| self.classifier_model = classifier_model | |
| self.classifier_tokenizer = classifier_tokenizer | |
| self.summarizer_model = summarizer_model | |
| self.summarizer_tokenizer = summarizer_tokenizer | |
| def classify_and_summarize(self, text): | |
| classifier = pipeline("text-classification", model=self.classifier_model, tokenizer=self.classifier_tokenizer, return_all_scores=True) | |
| summarizer = pipeline("summarization", model=self.summarizer_model, tokenizer=self.summarizer_tokenizer) | |
| # Classify the text | |
| classification_results = classifier(text)[0] | |
| # Determine the label with the highest score | |
| max_score = float('-inf') | |
| max_label = '' | |
| for result in classification_results: | |
| if result['score'] > max_score: | |
| max_score = result['score'] | |
| max_label = result['label'] | |
| # Summarize the text | |
| summary_results = summarizer(text, max_length=50, min_length=25, do_sample=False) | |
| return max_label, max_score, summary_results[0]['summary_text'] | |
| def from_pretrained(cls, classifier_path, summarizer_path): | |
| classifier_model = AutoModelForSequenceClassification.from_pretrained(classifier_path) | |
| classifier_tokenizer = AutoTokenizer.from_pretrained(classifier_path) | |
| summarizer_model = AutoModelForSeq2SeqLM.from_pretrained(summarizer_path) | |
| summarizer_tokenizer = AutoTokenizer.from_pretrained(summarizer_path) | |
| return cls(classifier_model, classifier_tokenizer, summarizer_model, summarizer_tokenizer) | |
| # Load the combined model | |
| classifier_path = "lqqqqqqqqq/FinetunedModelGr9" | |
| summarizer_path = "lqqqqqqqqq/SummarizeModelGr9" | |
| combined_model = CombinedModel.from_pretrained(classifier_path, summarizer_path) | |
| # Streamlit application title | |
| st.title("Twitter Text Classification and Summarization") | |
| st.write("Classification for 3 labels: negative, neutral, positive") | |
| st.markdown("Please enter at least 50 words & more than 4 sentences for better summarization") | |
| # Text input for user to enter the text to classify | |
| texts_input = st.text_area("Enter the texts to classify and summarize (one text per line)", "") | |
| # Perform text classification and summarization when the user clicks the "Classify" button | |
| if st.button("Classify & Summarize"): | |
| texts = texts_input.split('\n') | |
| for text in texts: | |
| text = text.strip() | |
| if text: # Check if text is not empty | |
| # Classify and summarize the input text | |
| label, score, summary = combined_model.classify_and_summarize(text) | |
| # Display the results | |
| st.write("Text:", text) | |
| st.write("Label:", label) | |
| st.write("Score:", score) | |
| st.write("Summary:", summary) | |
| st.write("---") | |
| else: | |
| st.write("Please enter some text(more than 4 sentences) to classify and summarize.") | |