Run FastAPI as subprocess in app.py

app.py

@@ -3,62 +3,118 @@ import requests
 import json
 import base64
 import io
-import subprocess
-import time
-import os
+from deep_translator import GoogleTranslator
+from gtts import gTTS
+import utils
 
-API_BASE_URL = "http://localhost:8000/api"
+# Initialize translator
+translator = GoogleTranslator(source='en', target='hi')
 
-# Start FastAPI server in the background
-if "fastapi_process" not in st.session_state:
-    # Ensure api.py is in the same directory
-    process = subprocess.Popen(["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"], 
-                              stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-    st.session_state["fastapi_process"] = process
-    time.sleep(3)  # Give FastAPI time to start
-
-st.title("News Summarization App")
+# 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:
-        status = st.status("Fetching news...", expanded=True)
-        status.write(f"Fetching news for **{company_name}**...")
-        try:
-            response = requests.post(
-                f"{API_BASE_URL}/fetch_news",
-                json={"company_name": company_name},
-                timeout=120
-            )
-            response.raise_for_status()
+        with st.status("Fetching news...", expanded=True) as status:
+            st.write(f"Fetching news for **{company_name}**...")
+            
+            try:
+                # Call the utils function directly
+                file_name = utils.fetch_and_save_news(company_name)
+                
+                if not file_name:
+                    status.update(label="No news found", state="error")
+                    st.warning(f"No news found for {company_name}")
+                else:
+                    # Read the saved JSON file
+                    with open(file_name, "r", encoding="utf-8") as file:
+                        news_data = json.load(file)
+                    
+                    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))
+                    
+                    # 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")
+                    
+                    # 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()
+                        st.download_button(
+                            label="Download Hindi Audio",
+                            data=audio_bytes,
+                            file_name=f"{company_name}_sentiment_hindi.mp3",
+                            mime="audio/mp3"
+                        )
+                    except Exception as e:
+                        st.error(f"Error generating Hindi audio: {str(e)}")
             
-            news_data = response.json()
-            if not news_data or "Company" not in 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)
-                st.subheader(f"News Analysis for {news_data['Company']}")
-                # ... rest of your display logic ...
-        
-        except requests.exceptions.RequestException as e:
-            status.update(label="Connection error", state="error")
-            st.error(f"Error connecting to API: {str(e)}")
-            st.info("Check if the FastAPI backend is running correctly.")
-        except json.JSONDecodeError:
-            status.update(label="Invalid response", state="error")
-            st.error("Received invalid data from the API")
-        except Exception as e:
-            status.update(label="Processing error", state="error")
-            st.error(f"Error processing news data: {str(e)}")
+            except Exception as e:
+                status.update(label="Processing error", state="error")
+                st.error(f"Error processing news data: {str(e)}")
     else:
-        st.warning("Please enter a company name.")
-
-# Optional: Cleanup on app shutdown (not guaranteed in Spaces)
-def cleanup():
-    if "fastapi_process" in st.session_state:
-        st.session_state["fastapi_process"].terminate()
-
-import atexit
-atexit.register(cleanup)
+        st.warning("Please enter a company name.")