Upload app.py

app.py

@@ -0,0 +1,193 @@
+import streamlit as st
+import os
+import time
+from utils import fetch_news, analyze_sentiment, extract_topics, generate_tts
+import plotly.express as px
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+
+# Add custom CSS
+st.markdown("""
+    <style>
+    h1, .stTitle {color: #2E86C1; font-size: 2.5em; font-weight: bold;}
+    h2, .stSubheader {color: #1A5276; font-weight: bold;}
+    .stButton>button {background-color: #3498DB; color: white; border-radius: 5px; padding: 0.5em 1em;}
+    .stButton>button:hover {background-color: #2E86C1;}
+    .sentiment-positive {color: green; font-weight: bold;}
+    .sentiment-negative {color: red; font-weight: bold;}
+    .sentiment-neutral {color: gray; font-weight: bold;}
+    .sidebar .sidebar-content {position: sticky; top: 50%; padding: 10px;}
+    </style>
+""", unsafe_allow_html=True)
+
+st.title("News Summarizer and Sentiment Analyzer")
+
+company_name = st.text_input("Enter a company name to get a sentiment report of its recent news.", placeholder="e.g., Google, Meta", value="")
+
+if st.button("Analyze"):
+    with st.spinner("Fetching and analyzing news articles..."):
+        time.sleep(1)
+        articles_data = fetch_news(company_name)
+        if not articles_data:
+            st.error(f"No articles found for {company_name}. Check logs for details.")
+        else:
+            articles = []
+            sentiments = {"Positive": 0, "Negative": 0, "Neutral": 0}
+            positive_articles = []
+            negative_articles = []
+            neutral_articles = []
+
+            for article in articles_data:
+                summary = article["summary"].strip() or article["title"].split(" - ")[0].strip()
+                source = article["title"].split(" - ")[-1].strip() if " - " in article["title"] else ""
+                if source in summary:
+                    summary = summary.replace(source, "").strip()
+                summary = f"{summary.rstrip(' -')} - {source}"
+
+                sentiment = analyze_sentiment(summary)
+                topics = extract_topics(summary)
+                sentiments[sentiment] += 1
+
+                title = article["title"].split(" - ")[0].strip()
+                if sentiment == "Positive":
+                    positive_articles.append(title)
+                elif sentiment == "Negative":
+                    negative_articles.append(title)
+                else:
+                    neutral_articles.append(title)
+
+                articles.append({
+                    "Title": article["title"],
+                    "Summary": summary,
+                    "Sentiment": sentiment,
+                    "Topics": topics,
+                    "Link": article["link"],
+                    "PubDate": article["pub_date"]
+                })
+
+            import random
+            detailed_comparisons = [f"- News {i + 1} {article['Sentiment'].lower()}ly discusses {', '.join(article['Topics'])}"
+                                    for i, article in enumerate(articles)]
+            dominant_sentiment = max(sentiments, key=sentiments.get)
+            trends = f"{company_name} News Trends: {dominant_sentiment}"
+
+            total_articles = sum(sentiments.values())
+            sentiment_count = f"{sentiments['Positive']} positive, {sentiments['Negative']} negative, {sentiments['Neutral']} neutral"
+
+            intro_phrases = [
+                f"Spanning {total_articles} recent reports, the narrative surrounding {company_name} tilts {dominant_sentiment.lower()}, with {sentiment_count}.",
+                f"Across {total_articles} articles in recent coverage, {company_name}’s story emerges as predominantly {dominant_sentiment.lower()}, reflecting {sentiment_count}.",
+                f"Drawing from {total_articles} latest publications, {company_name}’s news landscape leans {dominant_sentiment.lower()}, underscored by {sentiment_count}."
+            ]
+            positive_phrases = [
+                f"With {len(positive_articles)} favorable accounts, {company_name} demonstrates notable progress, exemplified by '{random.choice(positive_articles) if positive_articles else 'no specific examples available'}'.",
+                f"Boasting {len(positive_articles)} positive developments, {company_name} showcases strength, as evidenced in '{random.choice(positive_articles) if positive_articles else 'no notable instances'}'.",
+                f"Highlighted by {len(positive_articles)} encouraging reports, {company_name} is forging ahead, with '{random.choice(positive_articles) if positive_articles else 'no standout reports'}' standing out."
+            ]
+            negative_phrases = [
+                f"However, {len(negative_articles)} troubling narratives raise concerns, including '{random.choice(negative_articles) if negative_articles else 'no specific concerns noted'}'.",
+                f"Yet, {len(negative_articles)} adverse reports signal challenges, such as '{random.choice(negative_articles) if negative_articles else 'no highlighted issues'}'.",
+                f"Nevertheless, {len(negative_articles)} concerning stories cast a shadow, notably '{random.choice(negative_articles) if negative_articles else 'no notable setbacks'}'."
+            ]
+            neutral_phrases = [
+                f"Additionally, {len(neutral_articles)} impartial updates provide context, such as '{random.choice(neutral_articles) if neutral_articles else 'no neutral updates available'}'.",
+                f"Meanwhile, {len(neutral_articles)} balanced accounts offer insight, including '{random.choice(neutral_articles) if neutral_articles else 'no balanced reports'}'.",
+                f"Furthermore, {len(neutral_articles)} objective pieces contribute details, like '{random.choice(neutral_articles) if neutral_articles else 'no objective details'}'."
+            ]
+            outlook_phrases_positive = [
+                f"In summary, {company_name} appears poised for a favorable trajectory.",
+                f"All told, {company_name} stands on the cusp of a promising future.",
+                f"Ultimately, {company_name} is positioned for an optimistic course ahead."
+            ]
+            outlook_phrases_negative = [
+                f"In conclusion, {company_name} confronts a challenging path forward.",
+                f"Overall, {company_name} navigates a formidable road ahead.",
+                f"To conclude, {company_name} faces a demanding horizon."
+            ]
+            outlook_phrases_mixed = [
+                f"In the final analysis, {company_name} balances opportunity and uncertainty.",
+                f"On balance, {company_name} presents a complex outlook moving forward.",
+                f"Ultimately, {company_name} reflects a blend of prospects and hurdles."
+            ]
+
+            final_text = random.choice(intro_phrases) + " "
+            if positive_articles:
+                final_text += random.choice(positive_phrases) + " "
+            if negative_articles:
+                final_text += random.choice(negative_phrases) + " "
+            if neutral_articles:
+                final_text += random.choice(neutral_phrases) + " "
+            if sentiments["Positive"] > sentiments["Negative"]:
+                final_text += random.choice(outlook_phrases_positive)
+            elif sentiments["Negative"] > sentiments["Positive"]:
+                final_text += random.choice(outlook_phrases_negative)
+            else:
+                final_text += random.choice(outlook_phrases_mixed)
+
+            st.session_state.result = {
+                "Company": company_name,
+                "Articles": articles,
+                "Comparative Sentiment Score": {
+                    "Sentiment Distribution": f"Positive: {sentiments['Positive']}, Negative: {sentiments['Negative']}, Neutral: {sentiments['Neutral']}",
+                    "Trends": trends,
+                    "Detailed Comparisons": "\n".join(detailed_comparisons)
+                },
+                "Final Sentiment Analysis": final_text.strip()
+            }
+
+if "result" in st.session_state:
+    result = st.session_state.result
+    if "error" in result:
+        st.error(result["error"])
+    else:
+        dist = result['Comparative Sentiment Score']['Sentiment Distribution']
+        sentiment_counts = {
+            "Positive": int(dist.split("Positive: ")[1].split(",")[0]),
+            "Negative": int(dist.split("Negative: ")[1].split(",")[0]),
+            "Neutral": int(dist.split("Neutral: ")[1])
+        }
+        fig = px.pie(
+            values=list(sentiment_counts.values()),
+            names=list(sentiment_counts.keys()),
+            title="Sentiment Distribution",
+            color_discrete_map={"Positive": "green", "Negative": "red", "Neutral": "gray"},
+            width=300,
+            height=300
+        )
+        fig.update_layout(margin=dict(t=40, b=0, l=0, r=0))
+        st.sidebar.plotly_chart(fig, use_container_width=True)
+
+        st.subheader(f"Analysis for {result['Company']}")
+        for i, article in enumerate(result["Articles"], 1):
+            st.write(f"**News {i}:** {article['PubDate']} [Read full article]({article['Link']})")
+            st.write(f"Summary: {article['Summary']}")
+            sentiment_class = f"sentiment-{article['Sentiment'].lower()}"
+            st.markdown(f"Sentiment: <span class='{sentiment_class}'>{article['Sentiment']}</span>", unsafe_allow_html=True)
+            st.write("")
+
+        st.subheader("Comparative Sentiment Analysis")
+        st.write("Detailed Comparisons:")
+        st.write(f"Sentiment Distribution: {result['Comparative Sentiment Score']['Sentiment Distribution']}")
+        st.markdown(f"**{result['Comparative Sentiment Score']['Trends']}**", unsafe_allow_html=True)
+        st.markdown(result["Comparative Sentiment Score"]["Detailed Comparisons"], unsafe_allow_html=True)
+
+        st.subheader("Final Sentiment Analysis")
+        st.write(result["Final Sentiment Analysis"])
+
+        language = st.selectbox("Select Audio Language", ["Hindi", "English"])
+        if st.button("Generate News Audio"):
+            with st.spinner("Generating audio..."):
+                audio_buffer = generate_tts(result["Final Sentiment Analysis"], 'hi' if language == "Hindi" else 'en')
+                if audio_buffer:
+                    st.audio(audio_buffer, format="audio/mp3")
+                else:
+                    st.error("Failed to generate audio. Check terminal logs.")
+
+    st.markdown("""
+        <p style="font-size: small; color: grey; text-align: center;">
+            Developed By: Krishna Prakash
+            <a href="https://www.linkedin.com/in/krishnaprakash-profile/" target="_blank">
+                <img src="https://img.icons8.com/ios-filled/30/0077b5/linkedin.png" alt="LinkedIn" style="vertical-align: middle; margin: 0 5px;"/>
+            </a>
+        </p>
+    """, unsafe_allow_html=True)