Update app.py

app.py

@@ -1,53 +1,53 @@
-import streamlit as st
-import os
-import json
-from ingestion import DocumentProcessor
-from llm import LLMProcessor
-from qa_engine import QAEngine
-
-# Set up Streamlit page
-st.set_page_config(page_title="AI-Powered Document QA", layout="wide")
-st.title("📄 AI-Powered Document QA")
-
-# Initialize processors
-document_processor = DocumentProcessor()
-llm_processor = LLMProcessor()
-qa_engine = QAEngine()
-
-# File uploader
-st.sidebar.header("Upload a PDF")
-uploaded_file = st.sidebar.file_uploader("Choose a PDF file", type=["pdf"])
-
-if uploaded_file:
-    # Save file to a temporary path
-    pdf_path = f"temp/{uploaded_file.name}"
-    os.makedirs("temp", exist_ok=True)
-    
-    with open(pdf_path, "wb") as f:
-        f.write(uploaded_file.read())
-
-    st.sidebar.success("✅ File uploaded successfully!")
-
-    # Process the document
-    with st.spinner("🔄 Processing document..."):
-        document_processor.process_document(pdf_path)
-    
-    st.sidebar.success("✅ Document processed successfully!")
-
-    # Query input
-    question = st.text_input("Ask a question from the document:", placeholder="What are the key insights?")
-
-    if st.button("🔍 Search & Answer"):
-        if question:
-            with st.spinner("🧠 Searching for relevant context..."):
-                answer = qa_engine.query(question)
-            
-            st.subheader("📝 Answer:")
-            st.write(answer)
-
-        else:
-            st.warning("⚠️ Please enter a question.")
-
-# Footer
-st.markdown("---")
-st.caption("🤖 Powered by ChromaDB + Groq LLM | Built with ❤️ using Streamlit")
+import streamlit as st
+import os
+import json
+from utils.ingestion import DocumentProcessor
+from utils.llm import LLMProcessor
+from utils.qa_engine import QAEngine
+
+# Set up Streamlit page
+st.set_page_config(page_title="AI-Powered Document QA", layout="wide")
+st.title("📄 AI-Powered Document QA")
+
+# Initialize processors
+document_processor = DocumentProcessor()
+llm_processor = LLMProcessor()
+qa_engine = QAEngine()
+
+# File uploader
+st.sidebar.header("Upload a PDF")
+uploaded_file = st.sidebar.file_uploader("Choose a PDF file", type=["pdf"])
+
+if uploaded_file:
+    # Save file to a temporary path
+    pdf_path = f"temp/{uploaded_file.name}"
+    os.makedirs("temp", exist_ok=True)
+    
+    with open(pdf_path, "wb") as f:
+        f.write(uploaded_file.read())
+
+    st.sidebar.success("✅ File uploaded successfully!")
+
+    # Process the document
+    with st.spinner("🔄 Processing document..."):
+        document_processor.process_document(pdf_path)
+    
+    st.sidebar.success("✅ Document processed successfully!")
+
+    # Query input
+    question = st.text_input("Ask a question from the document:", placeholder="What are the key insights?")
+
+    if st.button("🔍 Search & Answer"):
+        if question:
+            with st.spinner("🧠 Searching for relevant context..."):
+                answer = qa_engine.query(question)
+            
+            st.subheader("📝 Answer:")
+            st.write(answer)
+
+        else:
+            st.warning("⚠️ Please enter a question.")
+
+# Footer
+st.markdown("---")
+st.caption("🤖 Powered by ChromaDB + Groq LLM | Built with ❤️ using Streamlit")