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"""Streamlit frontend for the News Summarization application."""
import streamlit as st
import pandas as pd
import json
import os
import plotly.express as px
import altair as alt
from utils import (
analyze_company_data,
TextToSpeechConverter,
get_translator,
NewsExtractor,
SentimentAnalyzer,
TextSummarizer
)
# Set page config
st.set_page_config(
page_title="News Summarization App",
page_icon="📰",
layout="wide"
)
# Show loading message
with st.spinner("Initializing the application... Please wait while we load the models."):
# Initialize components
try:
st.success("Application initialized successfully!")
except Exception as e:
st.error(f"Error initializing application: {str(e)}")
st.info("Please try refreshing the page.")
def process_company(company_name):
"""Process company data directly."""
try:
# Call the analysis function directly from utils
data = analyze_company_data(company_name)
# Generate Hindi audio from final analysis
if data.get("final_sentiment_analysis"):
# Get the translator
translator = get_translator()
if translator:
try:
# Create a more detailed Hindi explanation
sentiment_explanation = f"""
{company_name} के समाचारों का विश्लेषण:
समग्र भावना: {data['final_sentiment_analysis']}
भावनात्मक विश्लेषण:
- सकारात्मक भावना: {data.get('comparative_sentiment_score', {}).get('sentiment_indices', {}).get('positivity_index', 0):.2f}
- नकारात्मक भावना: {data.get('comparative_sentiment_score', {}).get('sentiment_indices', {}).get('negativity_index', 0):.2f}
- भावनात्मक तीव्रता: {data.get('comparative_sentiment_score', {}).get('sentiment_indices', {}).get('emotional_intensity', 0):.2f}
विश्वसनीयता स्कोर: {data.get('comparative_sentiment_score', {}).get('sentiment_indices', {}).get('confidence_score', 0):.2f}
"""
# Generate Hindi audio
tts_converter = TextToSpeechConverter()
audio_path = tts_converter.generate_audio(
sentiment_explanation,
f'{company_name}_summary'
)
data['audio_path'] = audio_path
except Exception as e:
print(f"Error generating Hindi audio: {str(e)}")
data['audio_path'] = None
else:
print("Translator not available")
data['audio_path'] = None
return data
except Exception as e:
st.error(f"Error processing company: {str(e)}")
return {"articles": [], "comparative_sentiment_score": {}, "final_sentiment_analysis": "", "audio_path": None}
def main():
st.title("📰 News Summarization and Analysis")
# Sidebar
st.sidebar.header("Settings")
# Replace dropdown with text input
company = st.sidebar.text_input(
"Enter Company Name",
placeholder="e.g., Tesla, Apple, Microsoft, or any other company",
help="Enter the name of any company you want to analyze"
)
if st.sidebar.button("Analyze") and company:
if len(company.strip()) < 2:
st.sidebar.error("Please enter a valid company name (at least 2 characters)")
else:
with st.spinner("Analyzing news articles..."):
try:
# Process company data
data = process_company(company)
if not data["articles"]:
st.error("No articles found for analysis.")
return
# Display Articles
st.header("📑 News Articles")
for idx, article in enumerate(data["articles"], 1):
with st.expander(f"Article {idx}: {article['title']}"):
# Display content with proper formatting
if article.get("content"):
st.markdown("**Content:**")
st.write(article["content"])
else:
st.warning("No content available for this article")
# Display summary if available
if article.get("summary"):
st.markdown("**Summary:**")
st.write(article["summary"])
# Display source
if article.get("source"):
st.markdown("**Source:**")
st.write(article["source"])
# Enhanced sentiment display
if "sentiment" in article:
sentiment_col1, sentiment_col2 = st.columns(2)
with sentiment_col1:
st.markdown("**Basic Sentiment:**")
st.write(article["sentiment"])
if "sentiment_score" in article:
st.write(f"**Confidence Score:** {article['sentiment_score']*100:.1f}%")
with sentiment_col2:
# Display fine-grained sentiment if available
if "fine_grained_sentiment" in article and article["fine_grained_sentiment"]:
st.markdown("**Detailed Sentiment:**")
fine_grained = article["fine_grained_sentiment"]
if "category" in fine_grained:
st.write(f"Category: {fine_grained['category']}")
if "confidence" in fine_grained:
st.write(f"Confidence: {fine_grained['confidence']*100:.1f}%")
# Display sentiment indices if available
if "sentiment_indices" in article and article["sentiment_indices"]:
st.markdown("**Sentiment Indices:**")
indices = article["sentiment_indices"]
# Create columns for displaying indices
idx_cols = st.columns(3)
# Display positivity and negativity in first column
with idx_cols[0]:
if "positivity_index" in indices:
st.markdown(f"**Positivity:** {indices['positivity_index']:.2f}")
if "negativity_index" in indices:
st.markdown(f"**Negativity:** {indices['negativity_index']:.2f}")
# Display emotional intensity and controversy in second column
with idx_cols[1]:
if "emotional_intensity" in indices:
st.markdown(f"**Emotional Intensity:** {indices['emotional_intensity']:.2f}")
if "controversy_score" in indices:
st.markdown(f"**Controversy:** {indices['controversy_score']:.2f}")
# Display confidence and ESG in third column
with idx_cols[2]:
if "confidence_score" in indices:
st.markdown(f"**Confidence:** {indices['confidence_score']:.2f}")
if "esg_relevance" in indices:
st.markdown(f"**ESG Relevance:** {indices['esg_relevance']:.2f}")
# Display entities if available
if "entities" in article and article["entities"]:
st.markdown("**Named Entities:**")
entities = article["entities"]
# Organizations
if "ORG" in entities and entities["ORG"]:
st.write("**Organizations:**", ", ".join(entities["ORG"]))
# People
if "PERSON" in entities and entities["PERSON"]:
st.write("**People:**", ", ".join(entities["PERSON"]))
# Locations
if "GPE" in entities and entities["GPE"]:
st.write("**Locations:**", ", ".join(entities["GPE"]))
# Money
if "MONEY" in entities and entities["MONEY"]:
st.write("**Financial Values:**", ", ".join(entities["MONEY"]))
# Display sentiment targets if available
if "sentiment_targets" in article and article["sentiment_targets"]:
st.markdown("**Sentiment Targets:**")
targets = article["sentiment_targets"]
for target in targets:
st.markdown(f"**{target['entity']}** ({target['type']}): {target['sentiment']} ({target['confidence']*100:.1f}%)")
st.markdown(f"> {target['context']}")
st.markdown("---")
# Display URL if available
if "url" in article:
st.markdown(f"**[Read More]({article['url']})**")
# Display Comparative Analysis
st.header("📊 Comparative Analysis")
analysis = data.get("comparative_sentiment_score", {})
# Sentiment Distribution
if "sentiment_distribution" in analysis:
st.subheader("Sentiment Distribution")
sentiment_dist = analysis["sentiment_distribution"]
try:
# Extract basic sentiment data
if isinstance(sentiment_dist, dict):
if "basic" in sentiment_dist and isinstance(sentiment_dist["basic"], dict):
basic_dist = sentiment_dist["basic"]
elif any(k in sentiment_dist for k in ['positive', 'negative', 'neutral']):
basic_dist = {k: v for k, v in sentiment_dist.items()
if k in ['positive', 'negative', 'neutral']}
else:
basic_dist = {'positive': 0, 'negative': 0, 'neutral': 1}
else:
basic_dist = {'positive': 0, 'negative': 0, 'neutral': 1}
# Calculate percentages
total_articles = sum(basic_dist.values())
if total_articles > 0:
percentages = {
k: (v / total_articles) * 100
for k, v in basic_dist.items()
}
else:
percentages = {k: 0 for k in basic_dist}
# Display as metrics
st.write("**Sentiment Distribution:**")
col1, col2, col3 = st.columns(3)
with col1:
st.metric(
"Positive",
basic_dist.get('positive', 0),
f"{percentages.get('positive', 0):.1f}%"
)
with col2:
st.metric(
"Negative",
basic_dist.get('negative', 0),
f"{percentages.get('negative', 0):.1f}%"
)
with col3:
st.metric(
"Neutral",
basic_dist.get('neutral', 0),
f"{percentages.get('neutral', 0):.1f}%"
)
# Create visualization
chart_data = pd.DataFrame({
'Sentiment': ['Positive', 'Negative', 'Neutral'],
'Count': [
basic_dist.get('positive', 0),
basic_dist.get('negative', 0),
basic_dist.get('neutral', 0)
],
'Percentage': [
f"{percentages.get('positive', 0):.1f}%",
f"{percentages.get('negative', 0):.1f}%",
f"{percentages.get('neutral', 0):.1f}%"
]
})
chart = alt.Chart(chart_data).mark_bar().encode(
y='Sentiment',
x='Count',
color=alt.Color('Sentiment', scale=alt.Scale(
domain=['Positive', 'Negative', 'Neutral'],
range=['green', 'red', 'gray']
)),
tooltip=['Sentiment', 'Count', 'Percentage']
).properties(
width=600,
height=300
)
text = chart.mark_text(
align='left',
baseline='middle',
dx=3
).encode(
text='Percentage'
)
chart_with_text = (chart + text)
st.altair_chart(chart_with_text, use_container_width=True)
except Exception as e:
st.error(f"Error creating visualization: {str(e)}")
# Display sentiment indices if available
if "sentiment_indices" in analysis and analysis["sentiment_indices"]:
st.subheader("Sentiment Indices")
indices = analysis["sentiment_indices"]
try:
if isinstance(indices, dict):
# Display as metrics in columns
cols = st.columns(3)
display_names = {
"positivity_index": "Positivity",
"negativity_index": "Negativity",
"emotional_intensity": "Emotional Intensity",
"controversy_score": "Controversy",
"confidence_score": "Confidence",
"esg_relevance": "ESG Relevance"
}
for i, (key, value) in enumerate(indices.items()):
if isinstance(value, (int, float)):
with cols[i % 3]:
display_name = display_names.get(key, key.replace("_", " ").title())
st.metric(display_name, f"{value:.2f}")
# Create visualization
chart_data = pd.DataFrame({
'Index': [display_names.get(k, k.replace("_", " ").title()) for k in indices.keys()],
'Value': [v if isinstance(v, (int, float)) else 0 for v in indices.values()]
})
chart = alt.Chart(chart_data).mark_bar().encode(
x='Value',
y='Index',
color=alt.Color('Index')
).properties(
width=600,
height=300
)
st.altair_chart(chart, use_container_width=True)
# Add descriptions
with st.expander("Sentiment Indices Explained"):
st.markdown("""
- **Positivity**: Measures the positive sentiment in the articles (0-1)
- **Negativity**: Measures the negative sentiment in the articles (0-1)
- **Emotional Intensity**: Measures the overall emotional content (0-1)
- **Controversy**: High when both positive and negative sentiments are strong (0-1)
- **Confidence**: Confidence in the sentiment analysis (0-1)
- **ESG Relevance**: Relevance to Environmental, Social, and Governance topics (0-1)
""")
except Exception as e:
st.error(f"Error creating indices visualization: {str(e)}")
# Display Final Analysis
st.header("📊 Final Analysis")
# Display overall sentiment analysis with enhanced formatting
if data.get("final_sentiment_analysis"):
st.markdown("### Overall Sentiment Analysis")
analysis_parts = data["final_sentiment_analysis"].split(". ")
if len(analysis_parts) >= 2:
# First sentence - Overall sentiment
st.markdown(f"**{analysis_parts[0]}.**")
# Second sentence - Key findings
st.markdown(f"**{analysis_parts[1]}.**")
# Third sentence - Additional insights (if available)
if len(analysis_parts) > 2:
st.markdown(f"**{analysis_parts[2]}.**")
else:
st.write(data["final_sentiment_analysis"])
# Add sentiment strength indicator
if data.get("ensemble_info"):
ensemble_info = data["ensemble_info"]
if "model_agreement" in ensemble_info:
agreement = ensemble_info["model_agreement"]
strength = "Strong" if agreement > 0.8 else "Moderate" if agreement > 0.6 else "Weak"
st.markdown(f"**Sentiment Strength:** {strength} (Agreement: {agreement:.2f})")
# Display ensemble model details
if data.get("ensemble_info"):
st.subheader("Ensemble Model Details")
ensemble_info = data["ensemble_info"]
# Create columns for model details
model_cols = st.columns(3)
with model_cols[0]:
st.markdown("**Primary Model:**")
if "models" in ensemble_info and "transformer" in ensemble_info["models"]:
model = ensemble_info["models"]["transformer"]
st.write(f"Sentiment: {model['sentiment']}")
st.write(f"Score: {model['score']:.3f}")
with model_cols[1]:
st.markdown("**TextBlob Analysis:**")
if "models" in ensemble_info and "textblob" in ensemble_info["models"]:
model = ensemble_info["models"]["textblob"]
st.write(f"Sentiment: {model['sentiment']}")
st.write(f"Score: {model['score']:.3f}")
with model_cols[2]:
st.markdown("**VADER Analysis:**")
if "models" in ensemble_info and "vader" in ensemble_info["models"]:
model = ensemble_info["models"]["vader"]
st.write(f"Sentiment: {model['sentiment']}")
st.write(f"Score: {model['score']:.3f}")
# Display ensemble agreement if available
if "model_agreement" in ensemble_info:
st.markdown(f"**Model Agreement:** {ensemble_info['model_agreement']:.3f}")
# Display Hindi audio player
st.subheader("🔊 Listen to Analysis (Hindi)")
if data.get("audio_path") and os.path.exists(data["audio_path"]):
st.audio(data["audio_path"])
else:
st.info("Generating Hindi audio summary...")
with st.spinner("Please wait while we generate the Hindi audio summary..."):
# Try to generate audio again
translator = get_translator()
if translator and data.get("final_sentiment_analysis"):
try:
# Translate final analysis to Hindi
translated_analysis = translator.translate(
data["final_sentiment_analysis"],
dest='hi'
).text
# Generate Hindi audio
tts_converter = TextToSpeechConverter()
audio_path = tts_converter.generate_audio(
translated_analysis,
f'{company}_summary'
)
if audio_path and os.path.exists(audio_path):
st.audio(audio_path)
else:
st.error("Hindi audio summary not available")
except Exception as e:
st.error(f"Error generating Hindi audio: {str(e)}")
else:
st.error("Hindi audio summary not available")
# Total Articles
if "total_articles" in analysis:
st.sidebar.info(f"Found {analysis['total_articles']} articles")
except Exception as e:
st.error(f"Error analyzing company data: {str(e)}")
print(f"Error: {str(e)}")
# Add a disclaimer
st.sidebar.markdown("---")
st.sidebar.markdown("### About")
st.sidebar.write("This app analyzes news articles and provides sentiment analysis for any company.")
if __name__ == "__main__":
main()
|