Update app.py

Adding agentic

app.py

@@ -1,52 +1,69 @@
 import streamlit as st
 from upload import upload_file_to_vectara
-from query import process_queries
+#from query import process_queries
 import os
+from st_app import launch_bot
+import nest_asyncio
+import asyncio
+import uuid
 
-# Ensure set_page_config is the first Streamlit command
-st.set_page_config(page_title="STC Bank Assistant", layout="centered")
-
-# Load external CSS for custom styling
-with open("style.css", "r") as f:
-    st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
-
-# Main UI layout
-st.markdown(
-    """
-    <h1>Welcome to the STC Bank Assistant</h1>
-
-    <div class="icon-container">
-        <!-- This yellowish box is the icon background -->
-        <div class="icon-box">💼</div>
-        <p class="icon-text">How may I help you?</p>
-    </div>
-
-    <h4>Add additional files here</h4>
-    """,
-    unsafe_allow_html=True
-)
-
-# Fetch credentials from environment variables
-customer_id = os.getenv("VECTARA_CUSTOMER_ID", "")
-api_key = os.getenv("VECTARA_API_KEY", "")
-corpus_id = os.getenv("VECTARA_CORPUS_ID", "")
-corpus_key = os.getenv("VECTARA_CORPUS_KEY", "")
-
-# File uploader with drag-and-drop text + limit note
-uploaded_files = st.file_uploader(
-    "Drag and drop file here\nLimit 200MB per file",
-    type=["pdf", "docx", "xlsx"],
-    accept_multiple_files=True
-)
-
-# If credentials exist and files are uploaded, handle them
-if uploaded_files and customer_id and api_key and corpus_id and corpus_key:
-    for file in uploaded_files:
-        response = upload_file_to_vectara(file, customer_id, api_key, corpus_key)
-        st.write(f"Uploaded {file.name}: {response}")
-
-    if st.button("Run Queries"):
-        results = process_queries(customer_id, api_key, corpus_key)
-        for question, answer in results.items():
-            st.subheader(question)
-            st.write(answer)
+
+
+# Setup for HTTP API Calls to Amplitude Analytics
+if 'device_id' not in st.session_state:
+    st.session_state.device_id = str(uuid.uuid4())
+
+if "feedback_key" not in st.session_state:
+    st.session_state.feedback_key = 0
+
+if __name__ == "__main__":
+    # Ensure set_page_config is the first Streamlit command
+    st.set_page_config(page_title="STC Bank Assistant", layout="centered")
+
+        # Load external CSS for custom styling
+    with open("style.css", "r") as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+    # Main UI layout
+    st.markdown(
+        """
+        <h1>Welcome to the STC Bank Assistant</h1>
+
+        <div class="icon-container">
+            <!-- This yellowish box is the icon background -->
+            <div class="icon-box">💼</div>
+            <p class="icon-text">How may I help you?</p>
+        </div>
+
+        <h4>Add additional files here</h4>
+        """,
+        unsafe_allow_html=True
+    )
+
+    # Fetch credentials from environment variables
+    customer_id = os.getenv("VECTARA_CUSTOMER_ID", "")
+    api_key = os.getenv("VECTARA_API_KEY", "")
+    corpus_id = os.getenv("VECTARA_CORPUS_ID", "")
+    corpus_key = os.getenv("VECTARA_CORPUS_KEY", "")
+
+    # File uploader with drag-and-drop text + limit note
+    uploaded_files = st.file_uploader(
+        "Drag and drop file here\nLimit 200MB per file",
+        type=["pdf", "docx", "xlsx"],
+        accept_multiple_files=True
+    )
+
+    # If credentials exist and files are uploaded, handle them
+    if uploaded_files and customer_id and api_key and corpus_id and corpus_key:
+        for file in uploaded_files:
+            response = upload_file_to_vectara(file, customer_id, api_key, corpus_key)
+            st.write(f"Uploaded {file.name}: {response}")
+
+    #if st.button("Run Queries"):
+    #    results = process_queries(customer_id, api_key, corpus_key)
+    #    for question, answer in results.items():
+    #        st.subheader(question)
+    #        st.write(answer)
+
+    nest_asyncio.apply()
+    asyncio.run(launch_bot())