Update interim.py

interim.py

@@ -1,9 +1,9 @@
+#fix workflow
 import os
 import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
 import networkx as nx
-import matplotlib.pyplot as plt
 from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain_openai import ChatOpenAI
 from langgraph.graph import MessagesState
@@ -24,7 +24,6 @@ if not tavily_api_key:
     raise ValueError("Missing required environment variable: TAVILY_API_KEY")
 
 # ------------------- Tool Definitions -------------------
-# Tavily Search Tool
 tavily_tool = TavilySearchResults(max_results=5)
 
 def multiply(a: int, b: int) -> int:
@@ -41,10 +40,9 @@ def divide(a: int, b: int) -> float:
         raise ValueError("Division by zero is not allowed.")
     return a / b
 
-# Combine tools
 tools = [add, multiply, divide, tavily_tool]
 
-# ------------------- LLM and System Message Setup -------------------
+# ------------------- LLM Setup -------------------
 llm = ChatOpenAI(model="gpt-4o-mini")
 llm_with_tools = llm.bind_tools(tools, parallel_tool_calls=False)
 sys_msg = SystemMessage(content="You are a helpful assistant tasked with performing arithmetic and search on a set of inputs.")
@@ -54,7 +52,6 @@ def assistant(state: MessagesState):
     """Assistant node to invoke LLM with tools."""
     return {"messages": [llm_with_tools.invoke([sys_msg] + state["messages"])]}
 
-# Define the graph
 app_graph = StateGraph(MessagesState)
 app_graph.add_node("assistant", assistant)
 app_graph.add_node("tools", ToolNode(tools))
@@ -66,28 +63,25 @@ react_graph = app_graph.compile()
 # ------------------- Streamlit Interface -------------------
 st.title("ReAct Agent")
 
-# Display the workflow graph
+# Display the workflow graph using NetworkX
 st.header("LangGraph Workflow Visualization")
 
-# Convert LangGraph workflow to NetworkX graph
 G = nx.DiGraph()
 G.add_edge("START", "assistant")
-G.add_edge("assistant", "tools", label="if tool condition")
-G.add_edge("tools", "assistant")
+G.add_edge("assistant", "tools", label="tools_condition")
+G.add_edge("tools", "assistant", label="loop back")
 
-# Draw the graph
 plt.figure(figsize=(10, 6))
 pos = nx.spring_layout(G, seed=42)
 nx.draw(G, pos, with_labels=True, node_size=3000, node_color="lightblue", font_size=10, font_weight="bold")
 nx.draw_networkx_edge_labels(G, pos, edge_labels={
     ("assistant", "tools"): "tools_condition",
-    ("tools", "assistant"): "loop back",
+    ("tools", "assistant"): "loop back"
 }, font_color="red")
 st.pyplot(plt)
 
-# Prompt user for inputs
-user_question = st.text_area("Enter your question:",
-                             placeholder="Example: 'Add 3 and 4. Multiply the result by 2. Divide it by 5.'")
+# User input
+user_question = st.text_area("Enter your question:", placeholder="Example: 'Add 3 and 4. Multiply the result by 2. Divide it by 5.'")
 
 if st.button("Submit"):
     if not user_question.strip():
@@ -98,15 +92,13 @@ if st.button("Submit"):
     messages = [HumanMessage(content=user_question)]
     response = react_graph.invoke({"messages": messages})
     
-    # Display results
     st.subheader("Responses")
     for m in response['messages']:
         st.write(m.content)
-
     st.success("Processing complete!")
 
-# Example Placeholder Suggestions
+# Example Questions
 st.sidebar.subheader("Example Questions")
 st.sidebar.write("- Add 3 and 4. Multiply the result by 2. Divide it by 5.")
 st.sidebar.write("- Tell me how many centuries Virat Kohli scored.")
-st.sidebar.write("- Search for the tallest building in the world.")
+st.sidebar.write("- Search for the tallest building in the world.")