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interfaces/__init__.py

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+# interfaces/__init__.py
+from .interface import create_interface
+
+__all__ = ["create_interface"]

interfaces/interface.py

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+import gradio as gr
+from langchain_core.messages import HumanMessage, messages_to_dict
+from langgraph.graph import StateGraph, END
+from agents.agents_nodes import agent_node, format_output, tool_node
+from utils.state_utils import AgentState
+
+def create_interface():
+
+    graph = StateGraph(AgentState)
+    graph.add_node("agent", agent_node)
+    graph.add_node("tool", tool_node)
+    graph.add_node("format", format_output)
+    
+    graph.set_entry_point("agent")
+    graph.add_edge("agent", "tool")
+    graph.add_edge("tool", "format")
+    graph.add_edge("format", END)
+    
+    app = graph.compile()
+
+    def process_query(query: str) -> dict:
+        try:
+            inputs = {"messages": [HumanMessage(content=query)]}
+            result = app.invoke(inputs)  
+            return messages_to_dict(result['messages'])[2]['data']['content']
+        except Exception as e:
+            return {"error": f"Execution error: {str(e)}"}
+
+
+    with gr.Blocks(title="Time Value of Money Calculator") as interface:
+        gr.Markdown("##  Time Value of Money Calculator")
+        gr.Markdown("Enter natural language queries about present/future value calculations")
+        
+        with gr.Row():
+            input_text = gr.Textbox(
+                label="Financial Question",
+                placeholder="E.g.: Present value of $3000 in 5 years at 8% interest?",
+                lines=3
+            )
+            output_json = gr.JSON(label="Result")  
+        
+        submit_btn = gr.Button("Calculate")
+        submit_btn.click(
+            fn=process_query,       
+            inputs=input_text,      
+            outputs=output_json     
+        )
+    return interface