Update app.py

app.py

@@ -1,6 +1,6 @@
 import os
 import streamlit as st
-import chatbot as demo_chat
+import model as demo_chat
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 st.title("Hi, I am Chatbot Philio :woman:")
@@ -18,6 +18,8 @@ scrollable_div_style = """
 </style>
 """
 
+llm_chain = demo_chat.chain()
+
 def render_chat_history(chat_history):
     #renders chat history
     for message in chat_history:
@@ -25,44 +27,10 @@ def render_chat_history(chat_history):
             with st.chat_message(message["role"]):
                 st.markdown(message["content"])
 
-# def generate_response(chat_history):
-#     tokenized_chat = tokenizer.apply_chat_template(chat_history, tokenize=True, add_generation_prompt=True, return_tensors="pt")
-#     outputs = model.generate(tokenized_chat, do_sample =True, max_new_tokens=50, temperature = 0.3, top_p = 0.85)
-#     answer = tokenizer.decode(outputs[0][tokenized_chat.shape[1]:],skip_special_tokens=True)
-#     final_answer = answer.split("<")[0]
-#     return final_answer
-
-#Application 
-#Langchain memory in session cache 
-#if 'memory' not in st.session_state:
-    #st.session_state.memory = demo_chat.demo_miny_memory()
-
-system_content = """
-You are an AI having conversation with a human. Below is an instruction that describes a task. 
-Write a response that appropriately completes the request.
-Reply with the most helpful and logic answer. During the conversation you need to ask the user 
-the following questions to complete the hotel booking task.
 
-1) Where would you like to stay and when?
-2) How many people are staying in the room?
-3) Do you prefer any ammenities like breakfast included or gym?
-4) What is your name, your email address and phone number? 
-
-Make sure you receive a logical answer from the user from every question to complete the hotel 
-booking process.
-"""
 #Check if chat history exists in this session
 if 'chat_history' not in st.session_state:
-    st.session_state.chat_history = [
-        {
-            "role": "system",
-            "content": system_content,
-        },
-        {"role": "ai", "content": "Hello, how can I help you today?"},
-    ] #Initialize chat history
-    
-# if 'model' not in st.session_state:
-#     st.session_state.model = model
+    st.session_state.chat_history = [] #Initialize chat history
     
 st.markdown('<div class="scrollable-div">', unsafe_allow_html=True) #add css style to container
 render_chat_history(st.session_state.chat_history)
@@ -75,7 +43,7 @@ if input_text := st.chat_input(placeholder="Here you can chat with our hotel boo
     st.session_state.chat_history.append({"role" : "human", "content" : input_text}) #append message to chat history
 
     with st.spinner("Generating response..."):
-        first_answer = demo_chat.demo_chain(input_text = input_text, history = st.session_state.chat_history)
+        first_answer = llm_chain.predict(input = input_text).strip()
         
         with st.chat_message("assistant"):
             st.markdown(first_answer)