update

agent.py

@@ -1,14 +1,17 @@
+import operator
 import os
 from langchain.chat_models import init_chat_model
 from langchain_core.messages import HumanMessage, SystemMessage, AIMessage, AnyMessage
-from langgraph.graph import add_messages
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langgraph.graph import add_messages, START, END, StateGraph
 
 from typing_extensions import TypedDict, Annotated
 
 
 class State(TypedDict):
     messages: Annotated[list, add_messages]
-    graph_state: str
+    aggregate: Annotated[list, operator.add]
+    # graph_state: str
 
 
 def get_llm():
@@ -20,33 +23,28 @@ def get_graph(llm):
     with open('prompts/system_prompt.md', 'r', encoding='utf-8') as markdown_file:
         system_prompt = markdown_file.read()
 
+    prompt_template = ChatPromptTemplate.from_messages(
+        [
+            ("system", system_prompt),
+            MessagesPlaceholder(variable_name="messages"),
+        ]
+    )
 
-    def node_1(state):
-        print("---Node 1---")
+    def call_model(state: State):
+        print("\n-------------------- Agent has been called -----------------------------------\n")
+        prompt = prompt_template.invoke(state["messages"])
+        response = llm.invoke(prompt)
 
-        messages = state['messages']
-
-        prompt: AnyMessage = [SystemMessage(content=system_prompt), messages]
-
-        response = llm.invoke([("system", prompt), messages])
-
-        return {"message": response}
-
-
-
-
-    from IPython.display import Image, display
-
-    from langgraph.graph import MessagesState, START, END, StateGraph
+        return {"messages": [response], "aggregate": ["Agent"]}
 
     # Build graph
     builder = StateGraph(State)
-    builder.add_node("node_1", node_1)
+    builder.add_node("Agent", call_model)
 
 
     # Logic
-    builder.add_edge(START, "node_1")
-    builder.add_edge("node_1", END)
+    builder.add_edge(START, "Agent")
+    builder.add_edge("Agent", END)
 
     return builder.compile()
 

app.py

@@ -3,6 +3,9 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from dotenv import load_dotenv
+
+from agent import *
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -12,34 +15,16 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        os.getenv("GROQ_API_KEY")
-        from langchain.chat_models import init_chat_model
-
-        self.model = init_chat_model("llama-3.3-70b-versatile", model_provider="groq")
+        load_dotenv()
+        self.llm = get_llm()
+        self.graph = get_graph(self.llm)
 
         print("BasicAgent initialized.")
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        # fixed_answer = "This is a default answer."
-        # fixed_answer = r'{"task_id": "task_id_1", "model_answer": "Between 2000 and 2009 (inclusive), Mercedes Sosa published three studio albums: Corazón Libre (2005), Cantora 1 (2009), and Cantora 2 (2009).", "reasoning_trace": "The different steps by which your model reached answer 1"}{"task_id": "task_id_2", "model_answer": "Answer 2 from your model", "reasoning_trace": "The different steps by which your model reached answer 2"}'
-        #fixed_answer = "I need to find how many studio albums Mercedes Sosa published between 2000 and 2009, inclusive. From the provided list: 2005: Corazón Libre, 2009: Cantora 1 and 2009: Cantora 2. There are three albums within the specified range. FINAL ANSWER: 3"
-        #print(f"Agent returning fixed answer: {fixed_answer}")
-        fixed_answer = self.model.invoke([("system", """You are tasked with answering questions from the GAIA benchmark for AI agents.
-
-Provide ONLY the precise answer to the question. Do not include explanations, reasoning, or any additional text. Be direct, specific, and concise to meet the strict exact-matching requirements of the GAIA benchmark.
-
-# Output Format
-
-- **Single-word or short-phrase answers:** If the question necessitates a brief answer, provide just that word or phrase.
-- **Numerical values:** Provide only the number when applicable, with no additional formatting or units unless specifically requested.
-- **Full sentences:** If the question expects a sentence, provide the exact sentence required with no extra characters, punctuation, or formatting.
-
-# Notes
-
-- Be aware of strict exact-matching requirements; even minor deviations can result in an incorrect response.
-- If any ambiguity exists in the phrasing of the input, respond with an answer that aligns with the GAIA benchmark's intended interpretation."""), ("user", question)])
+        response = self.graph.invoke({"messages": [HumanMessage(content="question"),]})
 
-        return fixed_answer.content
+        return response["messages"][-1].content
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """

app_legacy.py

@@ -0,0 +1,218 @@
+import os
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+
+# (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):
+        os.getenv("GROQ_API_KEY")
+        from langchain.chat_models import init_chat_model
+
+        self.model = init_chat_model("llama-3.3-70b-versatile", model_provider="groq")
+
+        print("BasicAgent initialized.")
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # fixed_answer = "This is a default answer."
+        # fixed_answer = r'{"task_id": "task_id_1", "model_answer": "Between 2000 and 2009 (inclusive), Mercedes Sosa published three studio albums: Corazón Libre (2005), Cantora 1 (2009), and Cantora 2 (2009).", "reasoning_trace": "The different steps by which your model reached answer 1"}{"task_id": "task_id_2", "model_answer": "Answer 2 from your model", "reasoning_trace": "The different steps by which your model reached answer 2"}'
+        #fixed_answer = "I need to find how many studio albums Mercedes Sosa published between 2000 and 2009, inclusive. From the provided list: 2005: Corazón Libre, 2009: Cantora 1 and 2009: Cantora 2. There are three albums within the specified range. FINAL ANSWER: 3"
+        #print(f"Agent returning fixed answer: {fixed_answer}")
+        fixed_answer = self.model.invoke([("system", """You are tasked with answering questions from the GAIA benchmark for AI agents.
+
+Provide ONLY the precise answer to the question. Do not include explanations, reasoning, or any additional text. Be direct, specific, and concise to meet the strict exact-matching requirements of the GAIA benchmark.
+
+# Output Format
+
+- **Single-word or short-phrase answers:** If the question necessitates a brief answer, provide just that word or phrase.
+- **Numerical values:** Provide only the number when applicable, with no additional formatting or units unless specifically requested.
+- **Full sentences:** If the question expects a sentence, provide the exact sentence required with no extra characters, punctuation, or formatting.
+
+# Notes
+
+- Be aware of strict exact-matching requirements; even minor deviations can result in an incorrect response.
+- If any ambiguity exists in the phrasing of the input, respond with an answer that aligns with the GAIA benchmark's intended interpretation."""), ("user", question)])
+
+        return fixed_answer.content
+
+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)

app_playground.ipynb

@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-04-26T20:02:07.770606Z",
-     "start_time": "2025-04-26T20:02:07.726139Z"
+     "end_time": "2025-04-27T11:27:04.428213Z",
+     "start_time": "2025-04-27T11:26:58.244180Z"
     }
    },
    "source": [
@@ -22,14 +22,121 @@
     "\n",
     "graph = get_graph(llm)\n"
    ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\dennis.binzen\\PycharmProjects\\Final_Assignment\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-27T11:27:04.828108Z",
+     "start_time": "2025-04-27T11:27:04.434834Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from IPython.display import Image, display\n",
+    "display(Image(graph.get_graph().draw_mermaid_png()))"
+   ],
+   "id": "da8311fc7f1acf6",
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-27T11:30:44.925757Z",
+     "start_time": "2025-04-27T11:30:44.688378Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "res = graph.invoke({\"messages\": [HumanMessage(content=\"Hello, how are you?\"),]})",
+   "id": "a9fdfecc1af0975e",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "-------------------- Agent has been called -----------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-04-27T11:32:02.443950Z",
+     "start_time": "2025-04-27T11:32:02.438038Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "res[\"messages\"][-1].content",
+   "id": "66f2dcaeb343c836",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"I'm doing well.\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": null,
+   "source": "",
+   "id": "8d3a94be44f4859a"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "3bf392ddf3ce5c96"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "abe6c705b7557bdc"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-04-26T19:33:33.529627Z",
-     "start_time": "2025-04-26T19:33:33.064005Z"
+     "end_time": "2025-04-27T11:18:19.448376Z",
+     "start_time": "2025-04-27T11:18:19.025565Z"
     }
    },
    "cell_type": "code",
@@ -43,7 +150,7 @@
     {
      "data": {
       "text/plain": [
-       "\"Hello! I'm just a language model, so I don't have feelings or emotions like humans do, but I'm functioning properly and ready to help with any questions or tasks you might have. How can I assist you today?\""
+       "\"Hello. I'm just a language model, so I don't have feelings or emotions like humans do, but I'm functioning properly and ready to assist you with any questions or topics you'd like to discuss. How can I help you today?\""
       ]
      },
      "execution_count": 3,

requirements.txt

@@ -5,5 +5,6 @@ langchain~=0.3.24
 dotenv~=0.9.9
 python-dotenv~=1.1.0
 typing_extensions~=4.13.2
-langchain-groq~=0.3.2
-langgraph~=0.3.34
+langgraph~=0.3.34
+langchain-core~=0.3.56
+ipython~=9.2.0

test.py

@@ -1 +0,0 @@
-print("Hello")