Update app.py

app.py

@@ -12,7 +12,7 @@ from typing import Any, Dict
 from langchain_openai import ChatOpenAI
 from langgraph.graph import StateGraph, START, END
 from langgraph.graph.message import add_messages
-from langchain.schema import HumanMessage, AIMessage
+from langchain.schema import HumanMessage, AIMessage, SystemMessage
 # Create a ToolNode that knows about your web_search function
 
 # (Keep Constants as is)
@@ -44,39 +44,41 @@ compiled_graph = graph.compile()
 
 # ─── 5) Define `respond_to_input` to call `compiled_graph.invoke` ───
 def respond_to_input(user_input: str) -> str:
-    """
-    In v0.4.7, create_react_agent expects:
-      state["messages"] == list[BaseMessage], typically starting with a HumanMessage.
+    # ① Describe your tools in a system prompt
+    system_msg = SystemMessage(
+        content=(
+            "You are an assistant with access to the following tools:\n"
+            "  1) web_search(query: str) → Returns the top search results for the query as text.\n"
+            "  2) parse_excel(path: str, sheet_name: str) → Reads an Excel file and returns its contents.\n"
+            "  3) ocr_image(path: str) → Runs OCR on an image and returns any detected text.\n\n"
+            "When you need to look something up on the internet, respond exactly with JSON:\n"
+            '  {"tool":"web_search","query":"<search terms>"}\n'
+            "If you need to parse an Excel file, respond with:\n"
+            '  {"tool":"parse_excel","path":"<file.xlsx>","sheet_name":"<SheetName>"}\n'
+            "If you need to OCR an image, respond with:\n"
+            '  {"tool":"ocr_image","path":"<image.png>"}\n'
+            "If no tool is needed, reply only with your final answer as plain text."
+        )
+    )
 
-    We feed it exactly that, then call compiled_graph.invoke().
-    Finally, we scan final_state["messages"] for the last AIMessage and return its .content.
-    """
-    # 5.a) Build the initial state with a single HumanMessage
+    # ② Start the conversation with that system prompt and the user‘s question
     initial_state = {
-    "messages": [
-        {"role":"system",
-        "content": "You are an assistant that has access to a tool called web_search(query: str). "
-                    "Whenever you need up-to-date facts, respond with JSON like "
-                    '{"tool":"web_search","query":"<your search terms>"}.'},
-        {"role":"user","content": user_input}
-    ]
+        "messages": [
+            system_msg,
+            HumanMessage(content=user_input)
+        ]
     }
-    
+
+    # ③ Invoke the compiled graph
     final_state = compiled_graph.invoke(initial_state)
 
-    # 5.c) Extract the last AIMessage from final_state["messages"]
-    #     (create_react_agent always appends its assistant replies as AIMessage)
-    assistant_messages = [
+    # ④ Collect the last assistant message (AIMessage)
+    assistant_texts = [
         msg.content
         for msg in final_state["messages"]
         if isinstance(msg, AIMessage)
     ]
-    if not assistant_messages:
-        return "❗️Agent did not return any AIMessage."
-
-    # Return the final AIMessage's content
-    return assistant_messages[-1]
-
+    return assistant_texts[-1] if assistant_texts else ""
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")