Update app.py

app.py

@@ -7,8 +7,6 @@ import os
 import torch
 import pickle
 import base64
-import re
-from pyngrok import ngrok
 from googleapiclient.discovery import build
 from google_auth_oauthlib.flow import InstalledAppFlow
 from google.auth.transport.requests import Request
@@ -54,8 +52,6 @@ if "candidates_message_shown" not in st.session_state:
     st.session_state.candidates_message_shown = False
 if "vector_db_message_shown" not in st.session_state:
     st.session_state.vector_db_message_shown = False
-if "pending_query" not in st.session_state:
-    st.session_state.pending_query = False
 
 def count_tokens(text):
     return len(text.split())
@@ -77,7 +73,6 @@ def reset_session_state():
     st.session_state.messages = []
     st.session_state.candidates_message_shown = False
     st.session_state.vector_db_message_shown = False
-    st.session_state.pending_query = False
     for filename in ["token.json", "data_chunks.pkl", "embeddings.pkl", "vector_store.index", "vector_database.pkl"]:
         if os.path.exists(filename):
             os.remove(filename)
@@ -400,50 +395,38 @@ def handle_user_query():
         # Append user message to chat
         st.session_state.messages.append({"role": "user", "content": user_input})
 
-        # Append assistant "thinking" message
-        st.session_state.messages.append({"role": "assistant", "content": "💭 Processing your query..."})
-
-        # Display chat messages
-        for msg in st.session_state.messages:
-            if msg["role"] == "user":
-                with st.chat_message("user"):
-                    st.markdown(msg["content"])
-            elif msg["role"] == "assistant":
-                with st.chat_message("assistant"):
-                    st.markdown(msg["content"])
-
         # Process the query
         process_candidate_emails(user_input, similarity_threshold)
 
-        # If there's a candidate context, call the LLM API
         if st.session_state.candidate_context:
+            # Send the query to the LLM API
             call_llm_api(user_input)
 
-        # Display chat messages again with updated AI response
-        for msg in st.session_state.messages:
-            if msg["role"] == "user":
-                with st.chat_message("user"):
-                    st.markdown(msg["content"])
-            elif msg["role"] == "assistant":
-                with st.chat_message("assistant"):
-                    st.markdown(msg["content"])
-
-        # Display matching email chunks in an expander
-        if st.session_state.raw_candidates:
-            with st.expander("🔎 Matching Email Chunks:", expanded=False):
-                for candidate, sim in st.session_state.raw_candidates:
-                    # Get a snippet (first 150 characters) of the body instead of full body content.
-                    body = candidate.get('body', 'No Content')
-                    snippet = (body[:150] + "...") if len(body) > 150 else body
-                    st.markdown(
-                        f"**From:** {candidate.get('sender','Unknown')}  <br>"
-                        f"**To:** {candidate.get('to','Unknown')}  <br>"
-                        f"**Date:** {candidate.get('date','Unknown')}  <br>"
-                        f"**Subject:** {candidate.get('subject','No Subject')}  <br>"
-                        f"**Body Snippet:** {snippet}  <br>"
-                        f"**Similarity:** {sim:.4f}",
-                        unsafe_allow_html=True
-                    )
+    # Display chat messages
+    for msg in st.session_state.messages:
+        if msg["role"] == "user":
+            with st.chat_message("user"):
+                st.markdown(msg["content"])
+        elif msg["role"] == "assistant":
+            with st.chat_message("assistant"):
+                st.markdown(msg["content"])
+
+    # Display matching email chunks in an expander
+    if st.session_state.raw_candidates:
+        with st.expander("🔎 Matching Email Chunks:", expanded=False):
+            for candidate, sim in st.session_state.raw_candidates:
+                # Get a snippet (first 150 characters) of the body instead of full body content.
+                body = candidate.get('body', 'No Content')
+                snippet = (body[:150] + "...") if len(body) > 150 else body
+                st.markdown(
+                    f"**From:** {candidate.get('sender','Unknown')}  <br>"
+                    f"**To:** {candidate.get('to','Unknown')}  <br>"
+                    f"**Date:** {candidate.get('date','Unknown')}  <br>"
+                    f"**Subject:** {candidate.get('subject','No Subject')}  <br>"
+                    f"**Body Snippet:** {snippet}  <br>"
+                    f"**Similarity:** {sim:.4f}",
+                    unsafe_allow_html=True
+                )
 
 # ===============================
 # 6. Main Application Logic