Update app.py

app.py

@@ -1,124 +1,142 @@
-import streamlit as st
-from src.document_processor import process_document
-from src.summarizer import TextSummarizer
-import logging
-from textblob import TextBlob  # Ensure this library is installed
-
-# Set up logging
-logging.basicConfig(level=logging.DEBUG)
-
-def main():
-    # Streamlit app configuration
-    st.set_page_config(
-        page_title="SumItUp | Document Summarizer",
-        page_icon="✍️",  # Or another icon that represents summarization
-        layout="wide"
-    )
-
-    st.title("✍️ SumItUp")
-    st.subheader("Intelligent Document Summarization Made Easy")
-
-    # Sidebar for configuration
-    st.sidebar.header("Summarization Settings")
-    summary_length = st.sidebar.slider(
-        "Summary Length",
-        min_value=100,
-        max_value=400,
-        value=250
-    )
-
-    # Tabs for different input methods
-    tab1, tab2 = st.tabs(["Paste Text", "Upload Document"])
-
-    # Initialize summarizer
-    summarizer = TextSummarizer()
-
-    # Function to classify sentiment
-    def classify_sentiment(polarity):
-        if polarity > 0:
-            return "Positive 😊"
-        elif polarity < 0:
-            return "Negative 😟"
-        else:
-            return "Neutral 😐"
-
-    # Tab 1: Direct Text Input
-    with tab1:
-        st.header("Direct Text Input")
-        text_input = st.text_area(
-            "Paste your text here:",
-            height=300,
-            help="Enter the text you want to summarize"
-        )
-
-        if st.button("Summarize Text", key="text_summarize"):
-            if text_input:
-                with st.spinner('Generating summary and sentiment analysis...'):
-                    try:
-                        # Generate summary
-                        summary = summarizer.generate_summary(
-                            text_input,
-                            max_length=summary_length,
-                            min_length=summary_length // 2  # Optional: set min_length proportionally
-                        )
-                        st.subheader("Summary")
-                        st.write(summary)
-
-                        # Perform sentiment analysis
-                        if text_input.strip():
-                            sentiment = TextBlob(text_input).sentiment
-                            sentiment_class = classify_sentiment(sentiment.polarity)
-                            st.subheader("Sentiment Analysis")
-                            st.write(f"Sentiment: {sentiment_class}")
-                            st.write(f"Polarity: {sentiment.polarity:.2f} (Range: -1 to 1)")
-                            st.write(f"Subjectivity: {sentiment.subjectivity:.2f} (Range: 0 to 1)")
-                        else:
-                            st.warning("No valid text for sentiment analysis.")
-
-                    except Exception as e:
-                        st.error(f"Summarization failed: {e}")
-            else:
-                st.warning("Please enter some text to summarize.")
-
-    # Tab 2: Document Upload
-    with tab2:
-        st.header("Document Upload")
-        uploaded_file = st.file_uploader(
-            "Choose a file",
-            type=['txt', 'pdf', 'docx'],
-            help="Upload a text, PDF, or Word document"
-        )
-
-        if uploaded_file is not None:
-            if st.button("Summarize Document", key="doc_summarize"):
-                with st.spinner('Processing, summarizing, and analyzing sentiment...'):
-                    try:
-                        # Process document
-                        document_text = process_document(uploaded_file)
-
-                        # Generate summary
-                        summary = summarizer.generate_summary(
-                            document_text,
-                            max_length=summary_length,
-                            min_length=summary_length // 2  # Optional: set min_length proportionally
-                        )
-                        st.subheader("Summary")
-                        st.write(summary)
-
-                        # Perform sentiment analysis
-                        if document_text.strip():
-                            sentiment = TextBlob(document_text).sentiment
-                            sentiment_class = classify_sentiment(sentiment.polarity)
-                            st.subheader("Sentiment Analysis")
-                            st.write(f"Sentiment: {sentiment_class}")
-                            st.write(f"Polarity: {sentiment.polarity:.2f} (Range: -1 to 1)")
-                            st.write(f"Subjectivity: {sentiment.subjectivity:.2f} (Range: 0 to 1)")
-                        else:
-                            st.warning("No valid text for sentiment analysis.")
-
-                    except Exception as e:
-                        st.error(f"Error processing document: {e}")
-
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+from src.document_processor import process_document
+from src.summarizer import TextSummarizer
+import logging
+from textblob import TextBlob  # Ensure this library is installed
+import http.server
+import threading
+
+
+# Set up logging
+logging.basicConfig(level=logging.DEBUG)
+
+class HealthCheck(http.server.BaseHTTPRequestHandler):
+    def do_GET(self):
+        if self.path == '/health':
+            self.send_response(200)
+            self.send_header('Content-type', 'text/plain')
+            self.end_headers()
+            self.wfile.write(b"OK")
+    
+    def do_HEAD(self):
+        if self.path == '/health':
+            self.send_response(200)
+            self.send_header('Content-type', 'text/plain')
+            self.end_headers()
+            
+def main():
+    threading.Thread(target=lambda: http.server.HTTPServer(('', 8080), HealthCheck).serve_forever(), daemon=True).start()
+    # Streamlit app configuration
+    st.set_page_config(
+        page_title="SumItUp | Document Summarizer",
+        page_icon="✍️",  # Or another icon that represents summarization
+        layout="wide"
+    )
+
+    st.title("✍️ SumItUp")
+    st.subheader("Intelligent Document Summarization Made Easy")
+
+    # Sidebar for configuration
+    st.sidebar.header("Summarization Settings")
+    summary_length = st.sidebar.slider(
+        "Summary Length",
+        min_value=100,
+        max_value=400,
+        value=250
+    )
+
+    # Tabs for different input methods
+    tab1, tab2 = st.tabs(["Paste Text", "Upload Document"])
+
+    # Initialize summarizer
+    summarizer = TextSummarizer()
+
+    # Function to classify sentiment
+    def classify_sentiment(polarity):
+        if polarity > 0:
+            return "Positive 😊"
+        elif polarity < 0:
+            return "Negative 😟"
+        else:
+            return "Neutral 😐"
+
+    # Tab 1: Direct Text Input
+    with tab1:
+        st.header("Direct Text Input")
+        text_input = st.text_area(
+            "Paste your text here:",
+            height=300,
+            help="Enter the text you want to summarize"
+        )
+
+        if st.button("Summarize Text", key="text_summarize"):
+            if text_input:
+                with st.spinner('Generating summary and sentiment analysis...'):
+                    try:
+                        # Generate summary
+                        summary = summarizer.generate_summary(
+                            text_input,
+                            max_length=summary_length,
+                            min_length=summary_length // 2  # Optional: set min_length proportionally
+                        )
+                        st.subheader("Summary")
+                        st.write(summary)
+
+                        # Perform sentiment analysis
+                        if text_input.strip():
+                            sentiment = TextBlob(text_input).sentiment
+                            sentiment_class = classify_sentiment(sentiment.polarity)
+                            st.subheader("Sentiment Analysis")
+                            st.write(f"Sentiment: {sentiment_class}")
+                            st.write(f"Polarity: {sentiment.polarity:.2f} (Range: -1 to 1)")
+                            st.write(f"Subjectivity: {sentiment.subjectivity:.2f} (Range: 0 to 1)")
+                        else:
+                            st.warning("No valid text for sentiment analysis.")
+
+                    except Exception as e:
+                        st.error(f"Summarization failed: {e}")
+            else:
+                st.warning("Please enter some text to summarize.")
+
+    # Tab 2: Document Upload
+    with tab2:
+        st.header("Document Upload")
+        uploaded_file = st.file_uploader(
+            "Choose a file",
+            type=['txt', 'pdf', 'docx'],
+            help="Upload a text, PDF, or Word document"
+        )
+
+        if uploaded_file is not None:
+            if st.button("Summarize Document", key="doc_summarize"):
+                with st.spinner('Processing, summarizing, and analyzing sentiment...'):
+                    try:
+                        # Process document
+                        document_text = process_document(uploaded_file)
+
+                        # Generate summary
+                        summary = summarizer.generate_summary(
+                            document_text,
+                            max_length=summary_length,
+                            min_length=summary_length // 2  # Optional: set min_length proportionally
+                        )
+                        st.subheader("Summary")
+                        st.write(summary)
+
+                        # Perform sentiment analysis
+                        if document_text.strip():
+                            sentiment = TextBlob(document_text).sentiment
+                            sentiment_class = classify_sentiment(sentiment.polarity)
+                            st.subheader("Sentiment Analysis")
+                            st.write(f"Sentiment: {sentiment_class}")
+                            st.write(f"Polarity: {sentiment.polarity:.2f} (Range: -1 to 1)")
+                            st.write(f"Subjectivity: {sentiment.subjectivity:.2f} (Range: 0 to 1)")
+                        else:
+                            st.warning("No valid text for sentiment analysis.")
+
+                    except Exception as e:
+                        st.error(f"Error processing document: {e}")
+
+
+if __name__ == "__main__":
+    main()