Update streamlit_app.py

streamlit_app.py

@@ -1,121 +1,121 @@
-import streamlit as st
-import os
-import tempfile
-# from src.RAG_builder import ProjectRAGGraph # Ensure your graph class is in your_filename.py
-
-from src.rag_graph import ProjectRAGGraph
-
-# from src.graph.rag_graph import ProjectRAGGraph
-# --- Page Config ---
-st.set_page_config(page_title="Project Analyst RAG", layout="wide")
-st.title("📄 Professional Project Analyst Chat")
-
-# --- Initialize Session State ---
-if "rag_graph" not in st.session_state:
-    st.session_state.rag_graph = ProjectRAGGraph()
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-if "thread_id" not in st.session_state:
-    st.session_state.thread_id = "default_user_1" # Hardcoded for demo, could be unique per session
-
-# --- Sidebar: File Upload ---
-with st.sidebar:
-    st.header("Upload Documents")
-    uploaded_files = st.file_uploader(
-        "Upload Project PDFs", 
-        type="pdf", 
-        accept_multiple_files=True
-    )
-    
-    process_button = st.button("Process Documents")
-
-    if process_button and uploaded_files:
-        with st.spinner("Processing PDFs..."):
-            pdf_paths = []
-            original_names = [] # <--- Add this
-            for uploaded_file in uploaded_files:
-                original_names.append(uploaded_file.name) # <--- Capture real name
-                with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
-                    tmp.write(uploaded_file.getvalue())
-                    pdf_paths.append(tmp.name)
-            
-            # Pass BOTH the paths and the original names
-            st.session_state.rag_graph.process_documents(
-                pdf_paths, 
-                original_names=original_names
-            )
-            
-            for path in pdf_paths:
-                os.remove(path)
-            st.success("Documents Indexed Successfully!")
-
-    # if process_button and uploaded_files:
-    #     with st.spinner("Processing PDFs..."):
-    #         # Create temporary file paths to pass to your PDF Loader
-    #         pdf_paths = []
-    #         for uploaded_file in uploaded_files:
-    #             with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
-    #                 tmp.write(uploaded_file.getvalue())
-    #                 pdf_paths.append(tmp.name)
-            
-    #         # Use your existing process_documents method
-    #         st.session_state.rag_graph.process_documents(pdf_paths)
-            
-    #         # Clean up temp files
-    #         for path in pdf_paths:
-    #             os.remove(path)
-                
-    #         st.success("Documents Indexed Successfully!")
-
-# --- Chat Interface ---
-# Display existing messages
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-        if "citations" in message and message["citations"]:
-            with st.expander("View Sources"):
-                for doc in message["citations"]:
-                    st.caption(f"Source: {doc.metadata.get('source', 'Unknown')} - Page: {doc.metadata.get('page', 'N/A')}")
-                    st.write(f"_{doc.page_content[:200]}..._")
-
-# User Input
-if prompt := st.chat_input("Ask a question about your projects..."):
-    # Check if vector store is ready
-    if st.session_state.rag_graph.vector_store is None:
-        st.error("Please upload and process documents first!")
-    else:
-        # Add user message to state
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        with st.chat_message("user"):
-            st.markdown(prompt)
-
-        # Generate Response using the Graph
-        with st.chat_message("assistant"):
-            with st.spinner("Analyzing..."):
-                # We need to call the graph. We'll modify the query return slightly to get citations
-                config = {"configurable": {"thread_id": st.session_state.thread_id}}
-                inputs = {"question": prompt}
-                
-                # Execute graph
-                result = st.session_state.rag_graph.workflow.invoke(inputs, config=config)
-                
-                answer = result["answer"]
-                context = result["context"] # These are the retrieved Document objects
-
-                st.markdown(answer)
-                
-                # Citations section
-                if context:
-                    with st.expander("View Sources"):
-                        for doc in context:
-                            source_name = os.path.basename(doc.metadata.get('source', 'Unknown'))
-                            page_num = doc.metadata.get('page', 0) + 1
-                            st.caption(f"📄 {source_name} (Page {page_num})")
-                            st.write(f"_{doc.page_content[:300]}..._")
-
-                # Add assistant response to state
-                st.session_state.messages.append({
-                    "role": "assistant", 
-                    "content": answer, 
-                    "citations": context
+import streamlit as st
+import os
+import tempfile
+# from src.RAG_builder import ProjectRAGGraph # Ensure your graph class is in your_filename.py
+
+from src.rag_graph import ProjectRAGGraph
+
+# from src.graph.rag_graph import ProjectRAGGraph
+# --- Page Config ---
+st.set_page_config(page_title="Project Report Analyzer", layout="wide")
+st.title("📄 Project Report Analyzer")
+
+# --- Initialize Session State ---
+if "rag_graph" not in st.session_state:
+    st.session_state.rag_graph = ProjectRAGGraph()
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+if "thread_id" not in st.session_state:
+    st.session_state.thread_id = "default_user_1" # Hardcoded for demo, could be unique per session
+
+# --- Sidebar: File Upload ---
+with st.sidebar:
+    st.header("Upload Documents")
+    uploaded_files = st.file_uploader(
+        "Upload Project PDFs", 
+        type="pdf", 
+        accept_multiple_files=True
+    )
+    
+    process_button = st.button("Process Documents")
+
+    if process_button and uploaded_files:
+        with st.spinner("Processing PDFs..."):
+            pdf_paths = []
+            original_names = [] # <--- Add this
+            for uploaded_file in uploaded_files:
+                original_names.append(uploaded_file.name) # <--- Capture real name
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+                    tmp.write(uploaded_file.getvalue())
+                    pdf_paths.append(tmp.name)
+            
+            # Pass BOTH the paths and the original names
+            st.session_state.rag_graph.process_documents(
+                pdf_paths, 
+                original_names=original_names
+            )
+            
+            for path in pdf_paths:
+                os.remove(path)
+            st.success("Documents Indexed Successfully!")
+
+    # if process_button and uploaded_files:
+    #     with st.spinner("Processing PDFs..."):
+    #         # Create temporary file paths to pass to your PDF Loader
+    #         pdf_paths = []
+    #         for uploaded_file in uploaded_files:
+    #             with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+    #                 tmp.write(uploaded_file.getvalue())
+    #                 pdf_paths.append(tmp.name)
+            
+    #         # Use your existing process_documents method
+    #         st.session_state.rag_graph.process_documents(pdf_paths)
+            
+    #         # Clean up temp files
+    #         for path in pdf_paths:
+    #             os.remove(path)
+                
+    #         st.success("Documents Indexed Successfully!")
+
+# --- Chat Interface ---
+# Display existing messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+        if "citations" in message and message["citations"]:
+            with st.expander("View Sources"):
+                for doc in message["citations"]:
+                    st.caption(f"Source: {doc.metadata.get('source', 'Unknown')} - Page: {doc.metadata.get('page', 'N/A')}")
+                    st.write(f"_{doc.page_content[:200]}..._")
+
+# User Input
+if prompt := st.chat_input("Ask a question about your projects..."):
+    # Check if vector store is ready
+    if st.session_state.rag_graph.vector_store is None:
+        st.error("Please upload and process documents first!")
+    else:
+        # Add user message to state
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # Generate Response using the Graph
+        with st.chat_message("assistant"):
+            with st.spinner("Analyzing..."):
+                # We need to call the graph. We'll modify the query return slightly to get citations
+                config = {"configurable": {"thread_id": st.session_state.thread_id}}
+                inputs = {"question": prompt}
+                
+                # Execute graph
+                result = st.session_state.rag_graph.workflow.invoke(inputs, config=config)
+                
+                answer = result["answer"]
+                context = result["context"] # These are the retrieved Document objects
+
+                st.markdown(answer)
+                
+                # Citations section
+                if context:
+                    with st.expander("View Sources"):
+                        for doc in context:
+                            source_name = os.path.basename(doc.metadata.get('source', 'Unknown'))
+                            page_num = doc.metadata.get('page', 0) + 1
+                            st.caption(f"📄 {source_name} (Page {page_num})")
+                            st.write(f"_{doc.page_content[:300]}..._")
+
+                # Add assistant response to state
+                st.session_state.messages.append({
+                    "role": "assistant", 
+                    "content": answer, 
+                    "citations": context
                 })