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chatbot-model-huggingface.py

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+import streamlit as st
+import chatbot_bedrock as demo_chat
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+st.title("Hi, I am Chatbot Philio :mermaid:")
+st.write("I am your hotel booking assistant for today.")
+
+hugging_face_key = st.sidebar.text_input('Enter Hugging Face Key to start the chat: ', type='password')
+
+# tokenizer = AutoTokenizer.from_pretrained("KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b")
+
+# [theme]
+# base="light"
+# primaryColor="#6b4bff"
+if(hugging_face_key != ""):
+    model_Loading = True
+    if model_Loading == True:
+        model = demo_chat.get_Model(hugging_face_key=hugging_face_key)
+        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)

chatbot_bedrock.py

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+import os
+from langchain import PromptTemplate, HuggingFaceHub, LLMChain
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationChain
+import langchain.globals
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def get_Model(hugging_face_key):
+    tokenizer = AutoTokenizer.from_pretrained("KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b",use_auth_token=hugging_face_key)
+    model = AutoModelForCausalLM.from_pretrained("KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b",use_auth_token=hugging_face_key).eval()
+    return model
+
+
+#Write function to connect to Bedrock
+# def demo_chatbot():
+#     # client = boto3.client('bedrock-runtime')
+    
+#     template = """Question: {question}
+
+#     Answer: Let's think step by step."""
+#     prompt = PromptTemplate(template=template, input_variables=["question"])
+#     llm=HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature":1e-10})
+
+#     question = "When was Google founded?"
+
+#     print(llm_chain.run(question))
+#     return demo_llm
+
+#test out the code with the Predicgt method
+    #return demo_llm.predict(input)
+# = demo_chatbot('What is the temperature in Nuremberg today?')
+#print(response)
+
+def demo_miny_memory(model):
+    # llm_data = get_Model(hugging_face_key)
+    memory = ConversationBufferMemory(llm = model,max_token_limit = 512)
+    return memory
+
+def demo_chain(input_text, memory,model):
+    # llm_data = get_Model(hugging_face_key)
+    llm_conversation = ConversationChain(llm=model,memory=memory,verbose=langchain.globals.get_verbose())
+
+    chat_reply = llm_conversation.predict(input=input_text)
+    return chat_reply