Create interim.py

interim.py

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+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
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import tools_condition
+from langgraph.prebuilt import ToolNode
+from langchain_core.messages import HumanMessage, SystemMessage
+
+# ------------------- Environment Variable Setup -------------------
+# Fetch API keys from environment variables
+openai_api_key = os.getenv("OPENAI_API_KEY")
+tavily_api_key = os.getenv("TAVILY_API_KEY")
+
+# Verify if API keys are set
+if not openai_api_key:
+    raise ValueError("Missing required environment variable: OPENAI_API_KEY")
+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:
+    """Multiply two numbers."""
+    return a * b
+
+def add(a: int, b: int) -> int:
+    """Add two numbers."""
+    return a + b
+
+def divide(a: int, b: int) -> float:
+    """Divide two numbers."""
+    if b == 0:
+        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 = 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.")
+
+# ------------------- LangGraph Workflow -------------------
+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))
+app_graph.add_edge(START, "assistant")
+app_graph.add_conditional_edges("assistant", tools_condition)
+app_graph.add_edge("tools", "assistant")
+react_graph = app_graph.compile()
+
+# ------------------- Streamlit Interface -------------------
+st.title("ReAct Agent")
+
+# Display the workflow graph
+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")
+
+# 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",
+}, 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.'")
+
+if st.button("Submit"):
+    if not user_question.strip():
+        st.error("Please enter a valid question.")
+        st.stop()
+    
+    st.info("Processing your question...")
+    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
+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.")