Delete app.py

app.py

@@ -1,130 +0,0 @@
-import gradio as gr
-import os
-import pinecone
-import openai
-
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.chat_models import ChatOpenAI
-from langchain.vectorstores import Pinecone
-
-from langchain.agents.openai_functions_agent.agent_token_buffer_memory import AgentTokenBufferMemory
-from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
-from langchain.schema.messages import SystemMessage
-from langchain.prompts import MessagesPlaceholder
-from langchain.agents import AgentExecutor
-from langchain.agents.agent_toolkits import create_retriever_tool
-
-print("CHECK - Pinecone vector db setup")
-
-# set up OpenAI environment vars and embeddings
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-embeddings = OpenAIEmbeddings()
-
-# initialize pinecone db
-index_name = "kellogg-course-assistant"
-
-pinecone.init(
-    api_key=os.getenv("PINECONE_API_KEY"),  # find at app.pinecone.io
-	environment=os.getenv("PINECONE_ENV"),  # next to api key in console
-)
-
-# load existing index
-vectorsearch = Pinecone.from_existing_index(index_name, embeddings)
-retriever = vectorsearch.as_retriever()
-
-print("CHECK - setting up conversational retrieval agent")
-
-# create LLM
-llm4 = ChatOpenAI(temperature = 0.1, model_name="gpt-4")
-llm35 = ChatOpenAI(temperature = 0.1, model_name="gpt-3.5-turbo-16k")
-llm = llm4
-
-# create retrieval tool
-tool = create_retriever_tool(
-    retriever, 
-    "search_kellogg_site",
-    "Searches and returns content from within the Kellogg website."
-)
-tools = [tool]
-
-# conversational retrieval agent component construction - memory, prompt template, agent, agent executor
-# This is needed for both the memory and the prompt
-memory_key = "history"
-memory = AgentTokenBufferMemory(memory_key=memory_key, llm=llm)
-# memory = AgentTokenBufferMemory(memory_key=memory_key, llm=llm, max_history=0, max_token_limit= 4000)
-
-system_message = SystemMessage(
-        content=(
-            "You are a helpful educational expert providing advice to students of the Northwestern business school Kellogg. "
-            "Use both your knowledge and the Kellogg site search tool to generate helpful answers for questions about courses and providing a list of suggested web course articles for more information. "
-            "Format your answer with distinct <h3>titles</h3> and <h3>subtitles</h3>, <b>emphasis</b>, <b>bold</b>, <i>italic<i>, <li>lists</li>, and tables *use html code*.  For lists, or bullet points, always start them by having a topic in <b>emphasis</b> before going into the description. Ensure to frequently take concepts and break them down into bullet points or lists following the emphasis directions that were just laid out."
-            "Do not include details of your intermediate steps in the final response. "
-            "At the end of your response, provide links to relevant web course articles returned by the retriever."
-        )
-)
-
-prompt = OpenAIFunctionsAgent.create_prompt(
-        system_message=system_message,
-        extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)]
-)
-
-agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
-agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory, verbose=True,return_intermediate_steps=True)
-
-# query = "I first noticed that data wasn’t flowing from splunk and received the following error message - No errors were recorded for the customer’s Splunk input"
-# result = agent_executor({"input":query})
-
-print("CHECK - setting up gradio chatbot UI")
-
-# build Gradio selectable options in Chat UI
-model_type=gr.Dropdown(choices=["gpt-4 + rag", 
-								"gpt-3.5-turbo + rag"], 
-								value="gpt-4 + rag",
-								type="index",
-								label="LLM Models"
-)
-
-# gradio chatbot UI
-def predict(message, history, model_type):
-	# clearing RAG memory
-	memory.clear()
-	
-	# specifying LLM to use
-	if (model_type==0):
-		llm = llm4
-	elif (model_type==1):
-		llm = llm35
-	else:
-		llm = llm4
-		
-	llm_response = agent_executor({"input":message})
-	
-	return llm_response["output"]
-
-# set up and run chat interface
-kellogg_agent = gr.ChatInterface(
-	fn=predict,
-	chatbot=gr.Chatbot(height=500),
-	textbox=gr.Textbox(placeholder="Ask me a question", container=False, scale=7),
-	title="Kellogg Course AI Assistant",
-	description="Please provide your questions about courses offered by Kellogg.",
-	additional_inputs=[model_type],
-	additional_inputs_accordion_name="AI Assistant Options:",
-	examples=[["Can you tell me about a marketing major?"],
-        ["What would I want from my career if I went towards marketing instead of strategy?"],
-        ["I'm interested in strategy. Can you give me a recommendation of courses I should consider over the next year?"],
-		["I'm wanting to know more about advertising. Can you recommend some courses on that subject?"],
-		["How many credits do I need to graduate?"],
-		["I loved the Competitive Strategy and industrial structure class. Can you tell me others like that one?"]],
-#    cache_examples=True,
-#    retry_btn=None,
-	undo_btn="Delete Previous",
-	clear_btn="Clear",
-)
-
-user_cred = os.environ.get("USER_CRED")
-pass_cred = os.environ.get("PASS_CRED")
-
-# start UI
-if __name__ == "__main__":
-	kellogg_agent.queue().launch(auth=(user_cred, pass_cred))