fix stop iternation

app.py

@@ -22,8 +22,17 @@ class BasicAgent:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
         messages = [HumanMessage(content=question)]
         result = self.graph.invoke({"messages": messages})
-        answer = result['messages'][-1].content
-        return answer
+        # Extract the final answer message from the state
+        # The final message should be the content of the last AIMessage
+        submitted_answer = "Could not get answer from agent output." # Default if extraction fails
+        if result and "messages" in result:
+            for msg in reversed(result["messages"]): # Look for the last AI message
+                # Check if the message is an AIMessage and has content
+                if isinstance(msg, AIMessage) and hasattr(msg, 'content') and msg.content:
+                    submitted_answer = msg.content
+                    break # Found the last AI message content
+
+        return submitted_answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """

backup/agent.py

@@ -0,0 +1,198 @@
+# import libraries for langgraph, huggingface
+import os
+from dotenv import load_dotenv
+from typing import TypedDict, List, Dict, Any, Optional, Annotated
+
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
+
+from langgraph.graph import StateGraph, MessagesState, START, END
+from langgraph.graph.message import add_messages
+from langchain_core.messages import SystemMessage, HumanMessage, AnyMessage, AIMessage
+from langchain_core.messages.ai import subtract_usage
+
+from langchain.tools import Tool
+from langchain_core.tools import tool
+from langchain_community.tools import WikipediaQueryRun
+from langchain_community.utilities import WikipediaAPIWrapper
+from langchain_community.utilities import SerpAPIWrapper
+from langchain_community.utilities import ArxivAPIWrapper
+from langchain_community.retrievers import BM25Retriever
+
+from langgraph.prebuilt import ToolNode, tools_condition
+
+
+# load environment variables
+load_dotenv()
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+
+# maths tool
+@tool
+def add(a:int, b:int) -> int:
+    """add two numbers.
+    args:
+        a: first int
+        b: second int
+    """
+    return a + b
+
+
+@tool
+def subtract(a:int, b:int) -> int:
+    """subtract two numbers.
+    args:
+        a: first int
+        b: second int
+    """
+    return a - b
+
+
+@tool
+def multiply(a:int, b:int) -> int:
+    """multiply two numbers.
+    args:
+        a: first int
+        b: second int
+    """
+    return a * b
+
+
+@tool
+def divide(a:int, b:int) -> float:
+    """divide two numbers.
+    args:
+        a: first int
+        b: second int
+    """
+    try:
+        # Attempt the division
+        result = a / b
+        return result
+    except ZeroDivisionError:
+        # Handle the case where b is zero
+        raise ValueError("Cannot divide by zero.")
+
+
+@tool
+def modulus(a:int, b:int) -> int:
+    """modulus remainder of two numbers.
+    args:
+        a: first int
+        b: second int
+    """
+    return a % b
+
+
+# wikipedia search tool
+@tool
+def search_wiki(query: str) -> Dict[str, str]:
+    """search wikipedia with a query
+    args:
+        query: a search query
+    """
+    docs = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
+    docs.run(query)
+    formatted_result = f'<Document source="{docs.metadata["source"]}" page="{docs.metadata.get("page", "")}"/>\n{docs.page_content}\n</Document>'
+    return {"wiki_results": formatted_result}
+
+
+# internet search tool
+@tool
+def search_web(query: str) -> Dict[str, str]:
+    """search internet with a query
+    args:
+        query: a search query
+    """
+    docs = SerpAPIWrapper()
+    docs.run(query)
+    formatted_result = f'<Document source="{docs.metadata["source"]}" page="{docs.metadata.get("page", "")}"/>\n{docs.page_content}\n</Document>'
+    return {"wiki_results": formatted_result}
+
+
+# ArXiv search tool
+@tool
+def search_arxiv(query: str) -> Dict[str, str]:
+    """search ArXiv for the paper with the given identifier
+    args:
+        query: a search identifier
+    """
+    arxiv = ArxivAPIWrapper()
+    docs = arxiv.run(query)
+    formatted_result = f'<Document source="{docs.metadata["source"]}" page="{docs.metadata.get("page", "")}"/>\n{docs.page_content}\n</Document>'
+    return {"wiki_results": formatted_result}
+
+
+# build retriever
+# bm25_retriever = BM25Retriever.from_documents(docs)
+
+
+# load system prompt from file
+with open("system_prompt.txt", "r", encoding="utf-8") as f:
+    system_prompt = f.read()
+
+
+# init system message
+sys_msg = SystemMessage(content=system_prompt)
+
+
+tools = [
+    add,
+    subtract,
+    multiply,
+    divide,
+    modulus,
+    search_wiki,
+    search_web,
+    search_arxiv
+]
+
+
+# build graph function
+def build_graph():
+    # llm
+    llm = HuggingFaceEndpoint(
+        repo_id = "microsoft/Phi-4-reasoning-plus",
+        huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
+    )
+
+    chat = ChatHuggingFace(llm=llm, verbose=False)
+
+    # bind tools to llm
+    chat_with_tools = chat.bind_tools(tools)
+
+    # generate AgentState and Agent graph
+    class AgentState(TypedDict):
+        messages: Annotated[list[AnyMessage], add_messages]
+
+    def assistant(state: AgentState):
+        return {
+            "messages": [chat_with_tools.invoke(state["messages"])],
+        }
+
+    # build graph
+    builder = StateGraph(AgentState)
+
+    # define nodes
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+
+    # define edges
+    builder.add_edge(START, "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        # If the latest message requires a tool, route to tools
+        # Otherwise, provide a direct response
+        tools_condition,
+    )
+    builder.add_edge("tools", "assistant")
+
+    return builder.compile()
+
+
+if __name__ == "__main__":
+    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
+    graph = build_graph()
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()

backup/app.py

@@ -0,0 +1,202 @@
+import os
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+
+from langchain_core.messages import HumanMessage
+from agent import build_graph
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.graph = build_graph()
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        messages = [HumanMessage(content=question)]
+        result = self.graph.invoke({"messages": messages})
+        answer = result['messages'][-1].content
+        return answer
+
+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
+
+    if profile:
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
+
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent ( modify this part to create your agent)
+    try:
+        agent = BasicAgent()
+    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)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+             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...")
+    for item in questions_data:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+        try:
+            submitted_answer = agent(question_text)
+            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+        except Exception as e:
+             print(f"Error running agent on task {task_id}: {e}")
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+
+    if not answers_payload:
+        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)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"Submission Successful!\n"
+            f"User: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+            f"Message: {result_data.get('message', 'No message received.')}"
+        )
+        print("Submission successful.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except requests.exceptions.HTTPError as e:
+        error_detail = f"Server responded with status {e.response.status_code}."
+        try:
+            error_json = e.response.json()
+            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+        except requests.exceptions.JSONDecodeError:
+            error_detail += f" Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.Timeout:
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.RequestException as e:
+        status_message = f"Submission Failed: Network error - {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except Exception as e:
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+
+# --- Build Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
+
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    # Removed max_rows=10 from DataFrame constructor
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
+if __name__ == "__main__":
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id_startup: # Print repo URLs if SPACE_ID is found
+        print(f"✅ SPACE_ID found: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)