Delete app.py

app.py

@@ -1,152 +0,0 @@
-from typing import Annotated, Sequence, TypedDict
-import functools
-import operator
-from bertopic import BERTopic
-from langchain.agents import AgentExecutor, create_openai_tools_agent
-from langchain_core.messages import BaseMessage, HumanMessage
-from langchain_openai import ChatOpenAI
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_core.output_parsers.openai_functions import JsonOutputFunctionsParser
-from langgraph.graph import END, StateGraph
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_experimental.tools import PythonREPLTool
-
-# Initialize tools
-tavily_tool = TavilySearchResults(max_results=5)
-python_repl_tool = PythonREPLTool()
-
-# Load BERTopic model
-topic_model = BERTopic.load("topic_model")
-
-# Function to create an agent
-def create_agent(llm: ChatOpenAI, tools: list, system_prompt: str):
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            ("system", system_prompt),
-            MessagesPlaceholder(variable_name="messages"),
-            MessagesPlaceholder(variable_name="agent_scratchpad"),
-        ]
-    )
-    agent = create_openai_tools_agent(llm, tools, prompt)
-    executor = AgentExecutor(agent=agent, tools=tools)
-    return executor
-
-# Function to define an agent node
-def agent_node(state, agent, name):
-    result = agent.invoke(state)
-    return {"messages": [HumanMessage(content=result["output"], name=name)]}
-
-# Define the Viewer agent using the BERTopic model
-def viewer_agent(state):
-    review = state["messages"][-1].content
-    index = topic_model.transform([review])[0][0]
-    answer = topic_model.topic_labels_.get(index)
-    return {"messages": [HumanMessage(content=answer, name="Viewer")]}
-
-# Define AgentState
-class AgentState(TypedDict):
-    messages: Annotated[Sequence[BaseMessage], operator.add]
-    next: str
-
-# Create LLM for the supervisor
-llm = ChatOpenAI(model="gpt-4-1106-preview")
-
-# Define the system prompt for the supervisor
-system_prompt = (
-    "You are a supervisor tasked with managing a conversation between the following workers: Researcher, Coder, Viewer. "
-    "Given the following user request, respond with the worker to act next. Each worker will perform a task and respond with their results and status. "
-    "When finished, respond with FINISH. If the request seems like a product review or sentiment analysis, route it to the Viewer."
-)
-
-# Define options
-options = ["FINISH", "Researcher", "Coder", "Viewer"]
-
-# Define the function for routing
-function_def = {
-    "name": "route",
-    "description": "Select the next role.",
-    "parameters": {
-        "title": "routeSchema",
-        "type": "object",
-        "properties": {
-            "next": {
-                "title": "Next",
-                "anyOf": [
-                    {"enum": options},
-                ],
-            }
-        },
-        "required": ["next"],
-    },
-}
-
-# Define the prompt for the supervisor
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", system_prompt),
-        MessagesPlaceholder(variable_name="messages"),
-        (
-            "system",
-            "Given the conversation above, who should act next? Or should we FINISH? Select one of: {options}",
-        ),
-    ]
-).partial(options=str(options), members="Researcher, Coder, Viewer")
-
-# Create the supervisor chain
-supervisor_chain = (
-    prompt
-    | llm.bind_functions(functions=[function_def], function_call="route")
-    | JsonOutputFunctionsParser()
-)
-
-# Define agents
-research_agent = create_agent(llm, [tavily_tool], "You are a web researcher.")
-research_node = functools.partial(agent_node, agent=research_agent, name="Researcher")
-
-code_agent = create_agent(
-    llm,
-    [python_repl_tool],
-    "You may generate safe python code to analyze data and generate charts using matplotlib.",
-)
-code_node = functools.partial(agent_node, agent=code_agent, name="Coder")
-
-viewer_node = functools.partial(viewer_agent)
-
-# Create the workflow
-workflow = StateGraph(AgentState)
-workflow.add_node("Researcher", research_node)
-workflow.add_node("Coder", code_node)
-workflow.add_node("Viewer", viewer_node)
-workflow.add_node("supervisor", supervisor_chain)
-
-# Add edges for each agent to report back to the supervisor
-members = ["Researcher", "Coder", "Viewer"]
-for member in members:
-    workflow.add_edge(member, "supervisor")
-
-# Add conditional edges
-conditional_map = {k: k for k in members}
-conditional_map["FINISH"] = END
-workflow.add_conditional_edges("supervisor", lambda x: x["next"], conditional_map)
-
-# Set the entry point
-workflow.set_entry_point("supervisor")
-
-# Compile the workflow
-graph = workflow.compile()
-
-# Testing the workflow
-for s in graph.stream(
-    {
-        "messages": [
-            HumanMessage(content="write a report of gopal who worked in 3 k technologies")
-        ]
-    }
-):
-    if "__end__" not in s:
-        print(s)
-        print("----")
-
-
-
-