Create interim.py

interim.py

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+import os
+import streamlit as st
+import json
+from langchain_openai import ChatOpenAI
+from langchain_core.tools import tool
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langgraph.graph import StateGraph, END
+from typing import TypedDict, Annotated, Sequence
+from langchain_core.messages import BaseMessage
+import operator
+import networkx as nx
+import matplotlib.pyplot as plt
+
+# Set API keys and validate credentials
+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("API keys not found. Please set OPENAI_API_KEY and TAVILY_API_KEY as environment variables.")
+    st.stop()
+
+# Initialize OpenAI LLM
+model = ChatOpenAI(temperature=0)
+
+# Define Tools
+@tool
+def multiply(first_number: int, second_number: int) -> int:
+    """Multiplies two integers together."""
+    return first_number * second_number
+
+@tool
+def search(query: str):
+    """Performs web search on the user query."""
+    tavily = TavilySearchResults(max_results=1)
+    result = tavily.invoke(query)
+    return result
+
+tools = [search, multiply]
+tool_map = {tool.name: tool for tool in tools}
+
+model_with_tools = model.bind_tools(tools)
+
+# Define Agent State class
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], operator.add]
+
+# Define workflow nodes
+def invoke_model(state):
+    messages = state['messages']
+    question = messages[-1]
+    return {"messages": [model_with_tools.invoke(question)]}
+
+def invoke_tool(state):
+    tool_calls = state['messages'][-1].additional_kwargs.get("tool_calls", [])
+    tool_details = None
+
+    for tool_call in tool_calls:
+        tool_details = tool_call
+
+    if tool_details is None:
+        raise Exception("No tool input found.")
+
+    selected_tool = tool_details.get("function").get("name")
+    st.sidebar.write(f"Selected tool: {selected_tool}")
+
+    if selected_tool == "search":
+        if 'human_loop' in st.session_state and st.session_state['human_loop']:
+            response = st.sidebar.radio("Proceed with web search?", ["Yes", "No"])
+            if response == "No":
+                raise ValueError("User canceled the search tool execution.")
+    
+    response = tool_map[selected_tool].invoke(json.loads(tool_details.get("function").get("arguments")))
+    return {"messages": [response]}
+
+def router(state):
+    tool_calls = state['messages'][-1].additional_kwargs.get("tool_calls", [])
+    if len(tool_calls):
+        return "tool"
+    else:
+        return "end"
+
+# Graph setup
+graph = StateGraph(AgentState)
+graph.add_node("agent", invoke_model)
+graph.add_node("tool", invoke_tool)
+graph.add_conditional_edges("agent", router, {"tool": "tool", "end": END})
+graph.add_edge("tool", END)
+graph.set_entry_point("agent")
+compiled_app = graph.compile()
+
+# Function to render graph with NetworkX
+def render_graph_nx(graph):
+    G = nx.DiGraph()
+    G.add_edge("agent", "tool", label="invoke tool")
+    G.add_edge("agent", "end", label="end condition")
+    G.add_edge("tool", "end", label="finish")
+
+    pos = nx.spring_layout(G, seed=42)
+    plt.figure(figsize=(8, 6))
+    nx.draw(G, pos, with_labels=True, node_color="lightblue", node_size=3000, font_size=10, font_weight="bold")
+    edge_labels = nx.get_edge_attributes(G, "label")
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=9)
+    plt.title("Workflow Graph")
+    st.pyplot(plt)
+
+# Streamlit UI
+st.title("LLM Tool Workflow Demo")
+st.write("This app demonstrates LLM-based tool usage with and without human intervention.")
+
+# Sidebar for options
+st.sidebar.header("Configuration")
+st.session_state['human_loop'] = st.sidebar.checkbox("Enable Human-in-the-Loop (For Search)", value=False)
+
+# Input prompt
+prompt = st.text_input("Enter your question:", "What is 24 * 365?")
+if st.button("Run Workflow"):
+    st.subheader("Execution Results")
+    try:
+        intermediate_outputs = []
+        for s in compiled_app.stream({"messages": [prompt]}):
+            intermediate_outputs.append(s)
+            st.write("Response:", list(s.values())[0])
+            st.write("---")
+
+        st.sidebar.write("### Intermediate Outputs")
+        for i, output in enumerate(intermediate_outputs):
+            st.sidebar.write(f"Step {i+1}: {output}")
+    except Exception as e:
+        st.error(f"Error occurred: {e}")
+
+    # Display Graph
+    st.subheader("Workflow Graph")
+    render_graph_nx(graph)