Update model.py

model.py

@@ -0,0 +1,57 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from langchain import HuggingFaceHub
+from langchain.llms.base import LLM
+from langchain.memory import ConversationBufferMemory,ConversationBufferWindowMemory
+from langchain.chains import LLMChain, ConversationChain
+from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.prompts import PromptTemplate, ChatPromptTemplate
+
+your_endpoint_url = "https://kp4xdy196cw81uf3.us-east-1.aws.endpoints.huggingface.cloud"
+
+llm = HuggingFaceEndpoint(
+    endpoint_url=f"{your_endpoint_url}",
+    huggingfacehub_api_token = ,
+    task = "text-generation",
+    max_new_tokens=128,
+    top_k=10,
+    top_p=0.95,
+    typical_p=0.95,
+    temperature=0.01,
+    repetition_penalty=1.03
+)
+#print(llm)
+
+def chat_template_prompt():
+    template = """
+    Do not repeat questions and do not generate answer for user/human.
+    
+    You are a helpful hotel booking asssitant.
+    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?
+    
+    When the booking task is completed, respond with "Thank you for choosing us.".
+    
+    {history}
+    
+    """
+    
+    system_prompt = SystemMessagePromptTemplate.from_template(template)
+    human_prompt = HumanMessagePromptTemplate.from_template("{input}")
+    chat_prompt = ChatPromptTemplate.from_messages([system_prompt, human_prompt])
+    return chat_prompt
+
+def chain():
+    #memory = ConversationBufferMemory(memory_key="history")
+    chat_prompt = chat_template_prompt()
+    memory = ConversationBufferWindowMemory(k=3, memory_key="history")
+    llm_chain = LLMChain(llm=llm, memory = memory, prompt = chat_prompt)
+    memory.load_memory_variables({}) #Initialize memory
+    return llm_chain
+