Create streamlit_app2.py

streamlit_app2.py

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+import streamlit as st
+import os
+import tempfile
+from src.RAG_builder import ProjectRAGGraph # Ensure your graph class is in your_filename.py
+
+# --- 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..."):
+            # 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
+                })