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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() |