Update app.py

app.py

@@ -1,19 +1,37 @@
 import streamlit as st
-from utils.auth import authenticate, generate_token, verify_token
+import os
+import sys
 from utils.database import initialize_vector_db, add_to_collection
 from utils.rag_utils import process_pdf, get_groq_response
-import os
+from typing import Optional
 
-# Configure for HF Spaces
+# Configure environment
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
-# Initialize FAISS DB
+# ---- Authentication Setup with Fallback ----
+try:
+    from utils.auth import authenticate, generate_token, verify_token
+    AUTH_ENABLED = True
+except ImportError as e:
+    st.warning(f"Authentication disabled due to: {str(e)}")
+    AUTH_ENABLED = False
+    
+    # Fallback dummy auth functions
+    def authenticate(*args, **kwargs): return True
+    def generate_token(*args, **kwargs): return "dummy_token"
+    def verify_token(*args, **kwargs): return True
+
+# ---- Session State Initialization ----
 if 'vector_db' not in st.session_state:
-    st.session_state.vector_db = initialize_vector_db()
+    with st.spinner("🔄 Initializing AI system..."):
+        st.session_state.vector_db = initialize_vector_db()
+
+if 'logged_in' not in st.session_state:
+    st.session_state.logged_in = False if AUTH_ENABLED else True
 
-# Authentication
+# ---- UI Components ----
 def login_section():
-    st.sidebar.title("🔐 Groq RAG Login")
+    st.sidebar.title("🔐 Admin Login")
     username = st.sidebar.text_input("Username")
     password = st.sidebar.text_input("Password", type="password")
     
@@ -21,38 +39,88 @@ def login_section():
         if authenticate(username, password):
             st.session_state.token = generate_token(username)
             st.session_state.logged_in = True
-            st.sidebar.success("Logged in!")
+            st.sidebar.success("Login successful!")
             st.rerun()
+        else:
+            st.sidebar.error("Invalid credentials")
+
+def pdf_uploader():
+    st.subheader("📤 Upload PDF Documents")
+    uploaded_file = st.file_uploader(
+        "Choose a PDF file", 
+        type="pdf",
+        label_visibility="collapsed"
+    )
+    
+    if uploaded_file:
+        with st.spinner("🔍 Extracting knowledge from PDF..."):
+            try:
+                chunks = process_pdf(uploaded_file)
+                if add_to_collection(chunks):
+                    st.success(f"✅ Processed {len(chunks)} text chunks!")
+            except Exception as e:
+                st.error(f"PDF processing failed: {str(e)}")
+
+def chat_interface():
+    st.subheader("💬 Chat with Your Documents")
+    
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+    
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+    
+    if prompt := st.chat_input("Ask about your documents"):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+        
+        with st.chat_message("assistant"):
+            with st.spinner("🤔 Thinking..."):
+                try:
+                    response = get_groq_response(prompt, st.session_state.vector_db)
+                    st.markdown(response)
+                    st.session_state.messages.append({"role": "assistant", "content": response})
+                except Exception as e:
+                    st.error(f"Error generating response: {str(e)}")
 
-# Main App
+# ---- Main App Flow ----
 def main_app():
     st.title("⚡ Groq-Powered RAG Chatbot")
+    st.caption("Upload PDFs and ask questions with lightning-fast responses")
     
-    # PDF Upload
-    uploaded_file = st.file_uploader("Upload PDF", type="pdf")
-    if uploaded_file:
-        with st.spinner("Extracting text..."):
-            chunks = process_pdf(uploaded_file)
-            if add_to_collection(chunks):
-                st.success(f"Added {len(chunks)} chunks to knowledge base!")
-    
-    # Chat
-    if prompt := st.chat_input("Ask about your PDFs"):
-        with st.spinner("Generating answer (Groq speed!)..."):
-            response = get_groq_response(prompt, st.session_state.vector_db)
-            st.chat_message("user").write(prompt)
-            st.chat_message("assistant").write(response)
-
-# Auth flow
-if not st.session_state.get("logged_in", False):
+    tab1, tab2 = st.tabs(["📄 Documents", "💬 Chat"])
+    
+    with tab1:
+        pdf_uploader()
+    
+    with tab2:
+        chat_interface()
+    
+    if st.sidebar.button("🔄 Reset Chat"):
+        st.session_state.messages = []
+        st.rerun()
+
+# ---- Authentication Flow ----
+if AUTH_ENABLED and not st.session_state.logged_in:
     login_section()
 else:
-    if verify_token(st.session_state.token):
+    if AUTH_ENABLED and not verify_token(st.session_state.get("token", "")):
+        st.error("Session expired")
+        st.session_state.logged_in = False
+        st.rerun()
+    else:
         main_app()
-        if st.sidebar.button("Logout"):
+        if AUTH_ENABLED and st.sidebar.button("🚪 Logout"):
             st.session_state.clear()
             st.rerun()
-    else:
-        st.error("Session expired")
-        st.session_state.clear()
-        login_section()
+
+# ---- Footer ----
+st.sidebar.markdown("---")
+st.sidebar.markdown("""
+**Technical Stack:**
+- Groq Cloud (Mixtral 8x7b)
+- FAISS Vector Store
+- Streamlit UI
+""")