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import os |
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import time |
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import requests |
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import streamlit as st |
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API_URL = "https://news-summarise-tts.hf.space" |
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st.title("News Summarization and Hindi TTS Application") |
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company = st.text_input("Enter Company Name", "") |
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if st.button("Fetch News"): |
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if company.strip() == "": |
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st.warning("Please enter a valid company name.") |
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else: |
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with st.spinner("Fetching and processing news..."): |
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try: |
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response = requests.get(f"{API_URL}/news/{company}") |
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if response.status_code == 200: |
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data = response.json() |
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st.header(f"News for {data['company']}") |
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for article in data["articles"]: |
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st.subheader(article.get("title", "No Title")) |
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st.markdown(f"**URL:** [Read More]({article.get('url', '#')})") |
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st.markdown(f"**Date:** {article.get('date', 'N/A')}") |
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st.markdown(f"**Sentiment:** {article.get('sentiment', 'Neutral')} (Score: {article.get('score', 0):.2f})") |
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st.markdown(f"**Excerpt:** {article.get('content','')[:300]}...") |
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st.markdown("---") |
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st.subheader("Comparative Sentiment Analysis") |
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comp_sent = data.get("comparative_sentiment", {}) |
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st.write({k: comp_sent[k] for k in ["Positive", "Negative", "Neutral"]}) |
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if "graph" in comp_sent and os.path.exists(comp_sent["graph"]): |
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st.image(comp_sent["graph"], caption="Sentiment Analysis Graph") |
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st.subheader("Final Combined Summary") |
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st.write(data.get("final_summary", "No summary available.")) |
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st.subheader("Hindi Summary") |
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st.write(data.get("hindi_summary", "")) |
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st.subheader("Hindi Summary Audio") |
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audio_path = data.get("tts_audio", None) |
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if audio_path and os.path.exists(audio_path): |
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with open(audio_path, "rb") as audio_file: |
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st.audio(audio_file.read(), format='audio/mp3') |
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else: |
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st.error("Audio file not found or TTS generation failed.") |
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else: |
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st.error("Failed to fetch news from the API. Please try again.") |
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except requests.exceptions.RequestException as e: |
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st.error(f"API error: {e}") |
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