Update app.py

app.py

@@ -7,6 +7,7 @@ from langchain.schema import (
     SystemMessage,
 )
 from langchain_community.chat_models.huggingface import ChatHuggingFace
+from transformers import pipeline
 
 st.title("Hi, I am Chatbot Philio :mermaid:")
 st.write("I am your hotel booking assistant for today.")
@@ -17,11 +18,14 @@ st.write("I am your hotel booking assistant for today.")
 # base="light"
 # primaryColor="#6b4bff"
 
-model = demo_chat.load_model()
-token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
+tokenizer, model = demo_chat.load_model()
+
+model_identifier = "KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b"
+task = "text-classification"  # Change this to your model's task
+
+# Load the model using the pipeline
+model_pipeline = pipeline(task, model=model,tokenizer=tokenizer)
 
-chat_model = ChatHuggingFace(llm=model, token=token)
-print(chat_model.model_id)
 
 #Application 
 with st.container():
@@ -43,7 +47,6 @@ with st.container():
         with st.chat_message(message["role"]):
             st.write(message["content"])
             
-    chat_model._to_chat_prompt(st.session_state.chat_history)
 
     #Set up input text field
     input_text = st.chat_input(placeholder="Here you can chat with our hotel booking model.")
@@ -54,8 +57,9 @@ with st.container():
             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= chat_model)
-        first_answer = chat_response.split("Human")[0] #Because of Predict it prints the whole conversation.Here we seperate the first answer only.
-
+        #first_answer = chat_response.split("Human")[0] #Because of Predict it prints the whole conversation.Here we seperate the first answer only.
+        first_answer = model_pipeline(st.session_state.chat_history)
+        
         with st.chat_message("assistant"):
             st.write(first_answer)
             st.session_state.chat_history.append({"role": "assistant", "content": first_answer})