Delete interim.py

interim.py

@@ -1,184 +0,0 @@
-import os
-import streamlit as st
-from dotenv import load_dotenv
-from langchain_openai import ChatOpenAI
-from langchain.agents import AgentExecutor, create_openai_tools_agent
-from langchain_core.messages import BaseMessage, HumanMessage
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_experimental.tools import PythonREPLTool
-from langchain_community.document_loaders import DirectoryLoader, TextLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceBgeEmbeddings
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langgraph.graph import StateGraph, END
-from langchain_core.documents import Document
-from typing import Annotated, Sequence, TypedDict
-import functools
-import operator
-from langchain_core.tools import tool
-from glob import glob
-
-# Load environment variables
-
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
-
-if not OPENAI_API_KEY or not TAVILY_API_KEY:
-    st.error("Please set OPENAI_API_KEY and TAVILY_API_KEY in your environment variables.")
-    st.stop()
-
-# Initialize API keys and LLM
-llm = ChatOpenAI(model="gpt-4-1106-preview", openai_api_key=OPENAI_API_KEY)
-
-# Utility Functions
-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)
-    return AgentExecutor(agent=agent, tools=tools)
-
-def agent_node(state, agent, name):
-    result = agent.invoke(state)
-    return {"messages": [HumanMessage(content=result["output"], name=name)]}
-
-@tool
-def RAG(state):
-    """Use this tool to execute RAG. If the question is related to Japan or Sports, this tool retrieves the results."""
-    st.session_state.outputs.append('-> Calling RAG ->')
-    question = state
-    template = """Answer the question based only on the following context:\n{context}\nQuestion: {question}"""
-    prompt = ChatPromptTemplate.from_template(template)
-    retrieval_chain = (
-        {"context": retriever, "question": RunnablePassthrough()} |
-        prompt |
-        llm |
-        StrOutputParser()
-    )
-    result = retrieval_chain.invoke(question)
-    return result
-
-# Load Tools
-tavily_tool = TavilySearchResults(max_results=5, tavily_api_key=TAVILY_API_KEY)
-python_repl_tool = PythonREPLTool()
-
-# Streamlit UI
-st.title("Multi-Agent w Supervisor")
-
-# Example questions for immediate testing
-example_questions = [
-    "Code hello world and print it to the terminal",
-    "What is James McIlroy aiming for in sports?",
-    "Fetch India's GDP over the past 5 years and draw a line graph.",
-    "Fetch Japan's GDP over the past 4 years from RAG, then draw a line graph."
-]
-
-# File Selection Section
-source_files = glob("source/*.txt")
-selected_files = st.multiselect("Select files from the source directory:", source_files, default=source_files[:2])
-
-uploaded_files = st.file_uploader("Or upload your TXT files:", accept_multiple_files=True, type=['txt'])
-
-# Combine Files
-all_docs = []
-if selected_files:
-    for file_path in selected_files:
-        loader = TextLoader(file_path)
-        all_docs.extend(loader.load())
-
-if uploaded_files:
-    for uploaded_file in uploaded_files:
-        content = uploaded_file.read().decode("utf-8")
-        all_docs.append(Document(page_content=content, metadata={"name": uploaded_file.name}))
-
-if not all_docs:
-    st.warning("Please select files from the source directory or upload TXT files.")
-    st.stop()
-
-# Process Documents
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=10, length_function=len)
-split_docs = text_splitter.split_documents(all_docs)
-
-embeddings = HuggingFaceBgeEmbeddings(model_name="BAAI/bge-base-en-v1.5", model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
-db = Chroma.from_documents(split_docs, embeddings)
-retriever = db.as_retriever(search_kwargs={"k": 4})
-
-# Create Agents
-research_agent = create_agent(llm, [tavily_tool], "You are a web researcher.")
-code_agent = create_agent(llm, [python_repl_tool], "You may generate safe python code to analyze data and generate charts using matplotlib.")
-RAG_agent = create_agent(llm, [RAG], "Use this tool when questions are related to Japan or Sports category.")
-
-research_node = functools.partial(agent_node, agent=research_agent, name="Researcher")
-code_node = functools.partial(agent_node, agent=code_agent, name="Coder")
-rag_node = functools.partial(agent_node, agent=RAG_agent, name="RAG")
-
-members = ["RAG", "Researcher", "Coder"]
-system_prompt = (
-    "You are a supervisor managing these workers: {members}. Respond with the next worker or FINISH. "
-    "Use RAG tool for Japan or Sports questions."
-)
-options = ["FINISH"] + members
-function_def = {
-    "name": "route", "description": "Select the next role.",
-    "parameters": {"title": "routeSchema", "type": "object", "properties": {"next": {"anyOf": [{"enum": options}]}}, "required": ["next"]}
-}
-prompt = ChatPromptTemplate.from_messages([
-    ("system", system_prompt),
-    MessagesPlaceholder(variable_name="messages"),
-    ("system", "Given the conversation above, who should act next? Select one of: {options}"),
-]).partial(options=str(options), members=", ".join(members))
-
-supervisor_chain = (prompt | llm.bind_functions(functions=[function_def], function_call="route") | JsonOutputFunctionsParser())
-
-# Workflow
-class AgentState(TypedDict):
-    messages: Annotated[Sequence[BaseMessage], operator.add]
-    next: str
-
-workflow = StateGraph(AgentState)
-workflow.add_node("Researcher", research_node)
-workflow.add_node("Coder", code_node)
-workflow.add_node("RAG", rag_node)
-workflow.add_node("supervisor", supervisor_chain)
-
-for member in members:
-    workflow.add_edge(member, "supervisor")
-conditional_map = {k: k for k in members}
-conditional_map["FINISH"] = END
-workflow.add_conditional_edges("supervisor", lambda x: x["next"], conditional_map)
-workflow.set_entry_point("supervisor")
-graph = workflow.compile()
-
-# Workflow Execution
-if 'outputs' not in st.session_state:
-    st.session_state.outputs = []
-
-user_input = st.text_area("Enter your task or question:", placeholder=example_questions[0])
-
-def run_workflow(task):
-    st.session_state.outputs.clear()
-    st.session_state.outputs.append(f"User Input: {task}")
-    for state in graph.stream({"messages": [HumanMessage(content=task)]}):
-        if "__end__" not in state:
-            st.session_state.outputs.append(str(state))
-            st.session_state.outputs.append("----")
-
-if st.button("Run Workflow"):
-    if user_input:
-        run_workflow(user_input)
-    else:
-        st.warning("Please enter a task or question.")
-
-st.subheader("Example Questions:")
-for example in example_questions:
-    st.text(f"- {example}")
-
-st.subheader("Workflow Output:")
-for output in st.session_state.outputs:
-    st.text(output)