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# Importing Libraries and functions from utils.py in approach_api
import streamlit as st
from approach_api.utils.news_extraction_api import extract_news
from approach_api.utils.news_summarisation import summarize_text
from approach_api.utils.news_sentiment import analyze_sentiment
from approach_api.utils.topic_extraction import preprocess_text, train_lda, extract_topic_words
from approach_api.utils.comparative_analysis import comparative_sentiment_analysis
from approach_api.utils.text_to_speech import text_to_speech
import os
# Function
def analyze_company_news(company):
st.write(f"Analyzing company: {company}")
with st.spinner("Fetching news articles..."):
articles = extract_news(company)
if not articles:
st.error("No news articles found. Try a different company.")
return None
st.write(f"Found {len(articles)} articles")
articles_data = []
texts = [article["content"] for article in articles]
with st.spinner("Performing sentiment analysis..."):
sentiment_results = analyze_sentiment(texts)
st.write(f"Sentiment analysis completed for {len(sentiment_results['Predicted Sentiment'])} articles")
for article, sentiment in zip(articles, sentiment_results["Predicted Sentiment"]):
summary = summarize_text(article["content"])
preprocessed_text = preprocess_text([article["content"]])
lda_model, dictionary = train_lda(preprocessed_text)
topic_words = extract_topic_words(lda_model)
articles_data.append({
"Title": article["title"],
"Summary": summary,
"Sentiment": sentiment,
"Topics": topic_words
})
with st.spinner("Performing comparative analysis..."):
analysis_result = comparative_sentiment_analysis(company, articles_data)
st.write("Comparative analysis completed")
st.write("Analysis result:", analysis_result)
final_summary = f"{company}’s latest news coverage is mostly {analysis_result['Final Sentiment Analysis']}."
with st.spinner("Generating Hindi TTS summary..."):
try:
audio_file = text_to_speech(final_summary)
if os.path.exists(audio_file):
st.write(f"TTS summary generated: {audio_file}")
else:
st.error("Failed to generate TTS summary")
audio_file = None
except Exception as e:
st.error(f"TTS generation failed: {str(e)}")
audio_file = None
return {
"Company": company,
"Articles": articles_data,
"Comparative Sentiment Score": analysis_result,
"Audio": audio_file
}
st.title("Company News Analysis")
company = st.text_input("Enter the company name for analysis:")
if st.button("Analyze") and company:
st.write(f"Starting analysis for: {company}")
result = analyze_company_news(company)
if result:
st.subheader(f"Analysis for {result['Company']}")
for article in result["Articles"]:
st.write(f"**Title:** {article['Title']}")
st.write(f"**Summary:** {article['Summary']}")
st.write(f"**Sentiment:** {article['Sentiment']}")
st.write(f"**Topics:** {', '.join(article['Topics'])}")
st.markdown("---")
st.subheader("Comparative Sentiment Score")
st.json(result["Comparative Sentiment Score"])
st.subheader("Hindi TTS Summary")
if result["Audio"]:
st.audio(result["Audio"], format="audio/mp3")
else:
st.warning("TTS summary not available")
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