Update app.py

app.py

@@ -10,41 +10,41 @@ st.write("I am your hotel booking assistant for today.")
 # [theme]
 # base="light"
 # primaryColor="#6b4bff"
-if(hugging_face_key != ""):
-    model_Loading = True
-    if model_Loading == True:
-        model = demo_chat.get_Model()
-        model_Loading = False
-
-    #Application 
-    with st.container():
-        st.markdown('<div class="scrollable-div">', unsafe_allow_html=True)
-        #Langchain memory in session cache 
-        if 'memory' not in st.session_state:
-            st.write("Memory is initilizing ...")
-            st.session_state.memory = demo_chat.demo_miny_memory(model)
-
-        #Check if chat history exists in this session
-        if 'chat_history' not in st.session_state:
-            st.session_state.chat_history = [ ] #Initialize chat history
-
-        #renders chat history
-        for message in st.session_state.chat_history: 
-            with st.chat_message(message["role"]):
-                st.write(message["content"])
-
-        #Set up input text field
-        input_text = st.chat_input(placeholder="Here you can chat with Llamma 2 model.")
-
-        if input_text:
-            with st.chat_message("user"):
-                st.write(input_text)
-                st.session_state.chat_history.append({"role" : "user", "content" : input_text}) #append message to chat history
-
-            chat_response = demo_chat.demo_chain(input_text=input_text, memory=st.session_state.memory, model= model)
-            first_answer = chat_response.split("Human")[0] #Because of Predict it prints the whole conversation.Here we seperate the first answer only.
-
-            with st.chat_message("assistant"):
-                st.write(first_answer)
-                st.session_state.chat_history.append({"role": "assistant", "content": first_answer})
-        st.markdown('</div>', unsafe_allow_html=True)
+
+model_Loading = True
+if model_Loading == True:
+    model = demo_chat.get_Model()
+    model_Loading = False
+
+#Application 
+with st.container():
+    st.markdown('<div class="scrollable-div">', unsafe_allow_html=True)
+    #Langchain memory in session cache 
+    if 'memory' not in st.session_state:
+        st.write("Memory is initilizing ...")
+        st.session_state.memory = demo_chat.demo_miny_memory(model)
+
+    #Check if chat history exists in this session
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history = [ ] #Initialize chat history
+
+    #renders chat history
+    for message in st.session_state.chat_history: 
+        with st.chat_message(message["role"]):
+            st.write(message["content"])
+
+    #Set up input text field
+    input_text = st.chat_input(placeholder="Here you can chat with Llamma 2 model.")
+
+    if input_text:
+        with st.chat_message("user"):
+            st.write(input_text)
+            st.session_state.chat_history.append({"role" : "user", "content" : input_text}) #append message to chat history
+
+        chat_response = demo_chat.demo_chain(input_text=input_text, memory=st.session_state.memory, model= model)
+        first_answer = chat_response.split("Human")[0] #Because of Predict it prints the whole conversation.Here we seperate the first answer only.
+
+        with st.chat_message("assistant"):
+            st.write(first_answer)
+            st.session_state.chat_history.append({"role": "assistant", "content": first_answer})
+    st.markdown('</div>', unsafe_allow_html=True)