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| import streamlit as st | |
| import pandas as pd | |
| import re | |
| from utils.prediction import predict_sentence | |
| def split_sentences_regex(text): | |
| # Clean the text | |
| text = re.sub(r'[\n\r]', ' ', text) # Remove newlines | |
| text = re.sub(r'["\']', '', text) # Remove quotes | |
| text = re.sub(r'\s+', ' ', text) # Normalize whitespace | |
| # More aggressive pattern that looks for sentence endings | |
| #pattern = r'[.!?]+[\s]+|[.!?]+$' | |
| pattern = r'[.]' | |
| # Split and clean resulting sentences | |
| sentences = [s.strip() for s in re.split(pattern, text) if s] | |
| # Filter out empty strings but keep sentences that don't start with capitals | |
| return [s for s in sentences if len(s) > 0] | |
| def split_sentences_with_abbrev(text): | |
| # Common abbreviations to ignore | |
| abbreviations = {'mr.', 'mrs.', 'dr.', 'sr.', 'jr.', 'vs.', 'e.g.', 'i.e.', 'etc.'} | |
| # Split initially by potential sentence endings | |
| parts = text.split('. ') | |
| sentences = [] | |
| current = parts[0] | |
| for part in parts[1:]: | |
| # Check if the previous part ends with an abbreviation | |
| ends_with_abbrev = any(current.lower().endswith(abbr) for abbr in abbreviations) | |
| if ends_with_abbrev: | |
| current = current + '. ' + part | |
| else: | |
| sentences.append(current) | |
| current = part | |
| sentences.append(current) | |
| return sentences | |
| def show_analysis(): | |
| st.title("Text Analysis") | |
| # Check model loading state | |
| if not all(key in st.session_state for key in ['model', 'label_encoder', 'tokenizer']): | |
| st.warning("Model components not found in session state") | |
| st.info("Please go to the Home page first to load the model") | |
| return | |
| if any(st.session_state[key] is None for key in ['model', 'label_encoder', 'tokenizer']): | |
| st.error("One or more model components failed to load") | |
| return | |
| # Get model components | |
| model = st.session_state.model | |
| label_encoder = st.session_state.label_encoder | |
| tokenizer = st.session_state.tokenizer | |
| # Text input section | |
| st.header("Analyze Your Text") | |
| user_text = st.text_area("Enter your text here (multiple sentences allowed):", height=150) | |
| if st.button("Analyze"): | |
| if not user_text: | |
| st.warning("Please enter some text to analyze") | |
| return | |
| with st.spinner("Analyzing text..."): | |
| try: | |
| # Split text into sentences | |
| sentences = split_sentences_regex(user_text) | |
| results = [] | |
| # Process each sentence | |
| for sentence in sentences: | |
| label, confidence = predict_sentence(model, sentence, tokenizer, label_encoder) | |
| results.append({ | |
| "Sentence": sentence, | |
| "Label": label, | |
| "Confidence": f"{confidence:.2%}" | |
| }) | |
| # Display results | |
| df = pd.DataFrame(results) | |
| st.dataframe(df) | |
| except Exception as e: | |
| st.error(f"Analysis failed: {str(e)}") | |
| if __name__ == "__main__": | |
| show_analysis() |