Delete model.py

model.py

@@ -1,41 +0,0 @@
-from langchain_core.utils.function_calling import convert_to_openai_function
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.memory import ConversationBufferWindowMemory
-from langchain.schema.runnable import RunnablePassthrough
-from langchain.agents.format_scratchpad import format_to_openai_functions
-from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
-from langchain.agents import AgentExecutor
-from langchain_groq import ChatGroq
-from functions import book_slot , check_slots , suggest_specialization , reschedule_event,delete_event
-
-def create_agent(general_prompt_template):
-    # print("get user Id**********************",user_id)
-    
-    API_KEY = "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"
-    tools = [book_slot , delete_event , check_slots , suggest_specialization , reschedule_event]
-    functions = [convert_to_openai_function(f) for f in tools]
-
-    llm = ChatGroq(
-    model="llama-3.1-70b-versatile",
-    temperature=0,
-    max_tokens=None,
-    timeout=None,
-    max_retries=2,
-    api_key=API_KEY
-        ).bind_functions(functions=functions)
- 
-    prompt = ChatPromptTemplate.from_messages([("system", general_prompt_template),
-                                               MessagesPlaceholder(variable_name="chat_history"), ("user", "{input}"),
-                                               MessagesPlaceholder(variable_name="agent_scratchpad")])
-
-
-
-    memory = ConversationBufferWindowMemory(memory_key="chat_history" , return_messages=True, k=5)
-
-    chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt | llm | OpenAIFunctionsAgentOutputParser()
-
-    agent_executor = AgentExecutor(
-        agent=chain, tools=tools, memory=memory, verbose=True)
-
-    return agent_executor
-# give me available slots at 17 september 2024 , book schedule at 10:30 AM 17 september 2024 ,