Run FastAPI as subprocess in app.py

app.py

@@ -1,115 +1,87 @@
 import streamlit as st
-import requests
-import json
-import base64
-import io
-from deep_translator import GoogleTranslator
-from gtts import gTTS
 import utils
+import os
+from gtts import gTTS
+import tempfile
+import re
+from deep_translator import GoogleTranslator
 
-# Initialize translator
-translator = GoogleTranslator(source='en', target='hi')
-
-# Streamlit app
 st.title("News Summarization and Text-to-Speech Application")
 
-# User input for company name
 company_name = st.text_input("Enter the company name:", "").strip().lower()
+
 if st.button("Fetch News"):
     if company_name:
-        with st.status("Fetching news...", expanded=True) as status:
-            st.write(f"Fetching news for **{company_name}**...")
+        # Status container for progress updates
+        status = st.status("Fetching news...", expanded=True)
+        status.write(f"Fetching news for **{company_name}**...")
+        
+        # Fetch and process news
+        file_name = utils.fetch_and_save_news(company_name)
+        
+        if os.path.exists(file_name):
+            status.update(label="News fetched successfully!", state="complete", expanded=False)
+            
+            st.subheader(f"News Analysis for {company_name.capitalize()}")
+            
+            # News Content Section
+            with open(file_name, "r", encoding="utf-8") as file:
+                text_content = file.read()
+            
+            with st.expander("View News Analysis", expanded=False):
+                st.text_area("Full Analysis", text_content, height=300)
+            
+            # Download Section
+            st.subheader("Download Data")
+            with open(file_name, "rb") as file:
+                st.download_button(
+                    label="Download Text File",
+                    data=file,
+                    file_name=file_name,
+                    mime="text/plain"
+                )
             
-            try:
-                # Call the utils function directly
-                news_data = utils.fetch_and_save_news(company_name)
+            # Final Sentiment Analysis
+            final_sentiment_line = ""
+            with open(file_name, "r", encoding="utf-8") as file:
+                content = file.read()
+                match = re.search(r'"Final Sentiment Analysis": "([^"]+)"', content)
+                if match:
+                    final_sentiment_line = match.group(1)
+            
+            if final_sentiment_line:
+                st.subheader("Final Sentiment Analysis")
+                st.info(final_sentiment_line)
                 
-                if not news_data:
-                    status.update(label="No news found", state="error")
-                    st.warning(f"No news found for {company_name}")
-                else:
-                    status.update(label="News fetched successfully!", state="complete", expanded=False)
-                    
-                    # Display news data
-                    st.subheader(f"News Analysis for {news_data['Company']}")
-                    
-                    # Articles section
-                    st.subheader("Articles")
-                    with st.expander("View Articles", expanded=False):
-                        for i, article in enumerate(news_data['Articles']):
-                            st.markdown(f"#### Article {i+1}: {article['Title']}")
-                            st.markdown(f"**Summary:** {article['Summary']}")
-                            st.markdown(f"**Sentiment:** {article['Sentiment']}")
-                            st.markdown(f"**Topics:** {', '.join(article['Topics'])}")
-                            st.divider()
-                    
-                    # Sentiment Distribution
-                    st.subheader("Sentiment Distribution")
-                    sentiment_data = news_data['Comparative Sentiment Score']['Sentiment Distribution']
-                    col1, col2, col3 = st.columns(3)
-                    col1.metric("Positive", sentiment_data['Positive'])
-                    col2.metric("Neutral", sentiment_data['Neutral'])
-                    col3.metric("Negative", sentiment_data['Negative'])
-                    
-                    # Topic Analysis
-                    st.subheader("Topic Analysis")
-                    with st.expander("View Topic Analysis", expanded=False):
-                        st.markdown("**Common Topics:**")
-                        st.write(", ".join(news_data['Topic Overlap']['Common Topics']))
-                        for key, value in news_data['Topic Overlap'].items():
-                            if key != "Common Topics":
-                                st.markdown(f"**{key}:**")
-                                st.write(", ".join(value))
+                # Hindi Audio Section
+                st.subheader("Hindi Audio for Final Sentiment Analysis")
+                try:
+                    translator = GoogleTranslator(source='en', target='hi')
+                    hindi_text = translator.translate(final_sentiment_line)
                     
-                    # Coverage Differences
-                    st.subheader("Coverage Differences")
-                    with st.expander("View Comparative Analysis", expanded=False):
-                        coverage_diff = news_data['Coverage Differences']
-                        if isinstance(coverage_diff, str):
-                            st.write(coverage_diff)  # Fallback for error cases
-                        else:
-                            formatted_text = '"Coverage Differences": [\n'
-                            for i, item in enumerate(coverage_diff.get("Coverage Differences", [])):
-                                formatted_text += "{\n"
-                                formatted_text += f'    "Comparison": "{item["Comparison"]}",\n'
-                                formatted_text += f'    "Impact": "{item["Impact"]}"\n'
-                                formatted_text += "}" + (",\n" if i < len(coverage_diff["Coverage Differences"]) - 1 else "\n")
-                            formatted_text += "]"
-                            st.code(formatted_text, language="json")
+                    tts = gTTS(text=hindi_text, lang='hi', slow=False)
+                    temp_audio_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
+                    tts.save(temp_audio_file.name)
                     
-                    # Final Sentiment Analysis
-                    st.subheader("Final Sentiment Analysis")
-                    st.info(news_data['Final Sentiment Analysis'])
-                    
-                    # Download JSON
-                    st.subheader("Download Data")
-                    st.download_button(
-                        label="Download JSON File",
-                        data=json.dumps(news_data, indent=4),
-                        file_name=f"{company_name}_news.json",
-                        mime="application/json"
-                    )
-                    
-                    # Hindi Audio
-                    st.subheader("Hindi Audio for Final Sentiment Analysis")
-                    try:
-                        hindi_text = translator.translate(news_data['Final Sentiment Analysis'])
-                        tts = gTTS(text=hindi_text, lang='hi')
-                        audio_io = io.BytesIO()
-                        tts.write_to_fp(audio_io)
-                        audio_io.seek(0)
-                        audio_bytes = audio_io.read()
+                    with open(temp_audio_file.name, "rb") as audio_file:
+                        audio_data = audio_file.read()
                         st.download_button(
                             label="Download Hindi Audio",
-                            data=audio_bytes,
+                            data=audio_data,
                             file_name=f"{company_name}_sentiment_hindi.mp3",
                             mime="audio/mp3"
                         )
-                    except Exception as e:
-                        st.error(f"Error generating Hindi audio: {str(e)}")
-            
-            except Exception as e:
-                status.update(label="Processing error", state="error")
-                st.error(f"Error processing news data: {str(e)}")
+                    
+                    # Clean up temporary file
+                    os.unlink(temp_audio_file.name)
+                    
+                except Exception as e:
+                    status.update(label="Audio processing error", state="error")
+                    st.error(f"Error generating Hindi audio: {str(e)}")
+            else:
+                st.warning("Could not find Final Sentiment Analysis in the text.")
+        else:
+            status.update(label="No news found", state="error")
+            st.warning(f"No relevant news articles found for {company_name}")
     else:
         st.warning("Please enter a company name.")