Update app.py

app.py

@@ -207,15 +207,24 @@
 #  3. This code replaces the original template entirely.
 #
 # =================================================================================================
+# =================================================================================================
+#  ✅ --- ✅  FINAL ASSESSMENT AGENT - INSTRUCTOR'S CORRECTED VERSION ✅ --- ✅
+# =================================================================================================
+#
+#  Instructions:
+#  1. Make sure your requirements.txt file matches the one provided by the instructor.
+#  2. Set your GROQ_API_KEY in the Hugging Face Space secrets.
+#  3. This code replaces the original template entirely.
+#
+# =================================================================================================
 
 import os
 import io
 import requests
-import inspect
 import pandas as pd
 import gradio as gr
 from contextlib import redirect_stdout
-from typing import TypedDict, Annotated, List, Union
+from typing import TypedDict, Annotated, List
 import operator
 
 # --- LangChain & LangGraph Imports ---
@@ -224,8 +233,7 @@ from langchain_core.tools import tool
 from langchain_groq import ChatGroq
 # from langchain_openai import ChatOpenAI #<-- Alternative LLM
 from langgraph.graph import StateGraph, END
-from langgraph.prebuilt import ToolExecutor
-
+from langgraph.prebuilt import ToolNode # <-- Corrected Import for modern LangGraph
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -301,7 +309,7 @@ def python_interpreter(code: str) -> str:
 
 #
 # ================================================================================================
-#  ✅ 2. CONFIGURE THE AGENT'S STATE, BRAIN (LLM), AND TOOL EXECUTOR
+#  ✅ 2. CONFIGURE THE AGENT'S STATE, BRAIN (LLM)
 # ================================================================================================
 #
 
@@ -312,9 +320,6 @@ class AgentState(TypedDict):
 # List of all the tools our agent can use
 tools = [web_search, read_file, python_interpreter]
 
-# The ToolExecutor is a helper class that runs the tools for us
-tool_executor = ToolExecutor(tools)
-
 # The "Brain" of our agent. We're using Groq for speed.
 # Make sure to set GROQ_API_KEY in your HF Space secrets
 llm = ChatGroq(model="llama3-70b-8192", temperature=0)
@@ -341,22 +346,11 @@ def call_model(state: AgentState) -> dict:
     # We return a dict, because this node will always be part of a graph
     return {"messages": [response]}
 
-# NODE 2: The Tool Node (call_tool)
-# This node executes the tool chosen by the LLM.
-def call_tool(state: AgentState) -> dict:
-    last_message = state['messages'][-1]  # Get the last message, which should be an AIMessage with tool calls
-    
-    # We construct an ToolMessage with the output of the tool call
-    action = last_message.tool_calls[0]
-    print(f"--- Preparing to call tool: {action['name']} with args {action['args']} ---")
-    tool_output = tool_executor.invoke(action)
-    return {"messages": [ToolMessage(content=str(tool_output), tool_call_id=action['id'])]}
-
 # EDGE: The Conditional Router (should_continue)
 # This function decides which node to go to next.
 def should_continue(state: AgentState) -> str:
     last_message = state['messages'][-1]
-    # If the LLM made a tool call, we route to the 'action' node (call_tool)
+    # If the LLM made a tool call, we route to the 'action' node to execute the tool
     if last_message.tool_calls:
         print("--- Decision: Call a tool ---")
         return "action"
@@ -367,16 +361,20 @@ def should_continue(state: AgentState) -> str:
 
 #
 # ================================================================================================
-#  ✅ 4. BUILD AND COMPILE THE GRAPH
+#  ✅ 4. BUILD AND COMPILE THE GRAPH (Corrected Version)
 # ================================================================================================
 #
 
+# The ToolNode is a pre-built node that executes tools for us.
+# It's the modern way to handle tool execution in LangGraph.
+tool_node = ToolNode(tools)
+
 # 1. Initialize the graph and add our state object
 workflow = StateGraph(AgentState)
 
-# 2. Add the two nodes we defined: 'agent' and 'action'
+# 2. Add the two nodes we need: the 'agent' and the 'action' (our tool_node)
 workflow.add_node("agent", call_model)
-workflow.add_node("action", call_tool)
+workflow.add_node("action", tool_node)
 
 # 3. Set the entry point of the graph. The first thing to run is the 'agent' node.
 workflow.set_entry_point("agent")
@@ -391,7 +389,7 @@ workflow.add_conditional_edges(
     }
 )
 
-# 5. Add a normal edge. After 'action' runs, it should always go back to 'agent'.
+# 5. Add a normal edge. After 'action' runs, it should always go back to 'agent' to reflect.
 workflow.add_edge('action', 'agent')
 
 # 6. Compile the graph into a runnable app.
@@ -407,32 +405,26 @@ app = workflow.compile()
 class GaiaAgent:
     def __init__(self):
         print("GaiaAgent initialized.")
-        # Any one-time setup can go here
         self.agent_app = app
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 100 chars): {question[:100]}...")
+        print(f"\n{'='*60}\nAgent received question (first 100 chars): {question[:100]}...\n{'='*60}")
         
         # The initial input for our graph is a list of messages.
         initial_input = {"messages": [HumanMessage(content=question)]}
         
         final_state = None
         # Let's add a loop limit to prevent infinite cycles
-        for i, step in enumerate(self.agent_app.stream(initial_input, {"recursion_limit": 25})):
-            # We'll just take the final state. The stream is useful for seeing intermediate steps.
+        for i, step in enumerate(self.agent_app.stream(initial_input, {"recursion_limit": 15})):
             if i == 0:
                 print("--- Starting Agentic Loop ---")
-            
-            # You can print keys to see what's happening at each step:
-            # print(f"Step {i}: {list(step.keys())}")
-            
             final_state = step
 
         # The final answer is in the last AIMessage of the 'messages' list
         final_answer_message = final_state['agent']['messages'][-1]
         final_answer = final_answer_message.content
         
-        print(f"--- Agent finished. Final Answer: {final_answer} ---")
+        print(f"\n--- Agent finished. Final Answer: {final_answer} ---\n")
         return final_answer
 
 #
@@ -446,8 +438,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    space_id = os.getenv("SPACE_ID")
 
     if profile:
         username= f"{profile.username}"
@@ -460,20 +451,15 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        # -------------------------------------------------------------------
-        # THIS IS THE ONLY CHANGE IN THIS FUNCTION: We now use our GaiaAgent
         agent = GaiaAgent()
-        # -------------------------------------------------------------------
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
-    # 2. Fetch Questions
     print(f"Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
@@ -483,18 +469,10 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
              print("Fetched questions list is empty.")
              return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
     except Exception as e:
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
-    # 3. Run your Agent
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
@@ -516,12 +494,10 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Prepare Submission
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
 
-    # 5. Submit
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
@@ -572,7 +548,7 @@ with gr.Blocks() as demo:
         """
         **Instructor's Note:** This space is now powered by a LangGraph agent.
         1.  Ensure your `GROQ_API_KEY` is set in the Space secrets.
-        2.  Make sure you have a `requirements.txt` file.
+        2.  Make sure you have a `requirements.txt` file with the specified versions.
         3.  Log in below and click 'Run Evaluation'. Good luck!
         """
     )