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.gitignore

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+# Environment variables
+.env
+
+# Python cache
+__pycache__/

README.md

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----
-title: Agent Test
-emoji: 🐨
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 6.5.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Template Final Assignment
+emoji: 🕵🏻‍♂️
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.25.2
+app_file: app.py
+pinned: false
+hf_oauth: true
+# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
+hf_oauth_expiration_minutes: 480
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

agent.py

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+from typing import TypedDict, Annotated, List
+import operator
+import os
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.messages import BaseMessage, HumanMessage
+from langgraph.graph import StateGraph, END, START
+from langgraph.prebuilt import ToolNode
+from langchain_tavily import TavilySearch
+import google.auth
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+# Set up Google credentials
+try:
+    _, project_id = google.auth.default()
+    os.environ["GOOGLE_CLOUD_PROJECT"] = project_id
+    os.environ["GOOGLE_CLOUD_LOCATION"] = "global"
+    os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "True"
+except google.auth.exceptions.DefaultCredentialsError:
+    print("Google Cloud credentials not found. Please configure your credentials.")
+    # You might want to fall back to an API key or raise an exception here
+    # For this example, we'll proceed, but it will likely fail if not configured
+    pass
+
+
+# 1. Define the state
+class AgentState(TypedDict):
+    messages: Annotated[List[BaseMessage], operator.add]
+
+# 2. Define the tools
+tools = [TavilySearch(max_results=1)]
+tool_node = ToolNode(tools)
+
+# 3. Define the model
+LLM = "gemini-1.5-flash"
+model = ChatGoogleGenerativeAI(model=LLM, temperature=0)
+model = model.bind_tools(tools)
+
+# 4. Define the agent node
+def should_continue(state):
+    messages = state['messages']
+    last_message = messages[-1]
+    # If there are no tool calls, then we finish
+    if not last_message.tool_calls:
+        return "end"
+    # Otherwise if there are tool calls, we continue
+    else:
+        return "continue"
+
+def call_model(state):
+    messages = state['messages']
+    response = model.invoke(messages)
+    # We return a list, because this will get added to the existing list
+    return {"messages": [response]}
+
+# 5. Create the graph
+workflow = StateGraph(AgentState)
+
+# Define the two nodes we will cycle between
+workflow.add_node("agent", call_model)
+workflow.add_node("action", tool_node)
+
+# Set the entrypoint as `agent`
+# This means that this node is the first one called
+workflow.add_edge(START, "agent")
+
+# We now add a conditional edge
+workflow.add_conditional_edges(
+    "agent",
+    should_continue,
+    {
+        "continue": "action",
+        "end": END,
+    },
+)
+
+# We now add a normal edge from `tools` to `agent`.
+# This means that after `tools` is called, `agent` node is called next.
+workflow.add_edge("action", "agent")
+
+# Finally, we compile it!
+# This compiles it into a LangChain Runnable,
+# meaning you can use it as you would any other runnable
+app = workflow.compile()
+
+
+class LangGraphAgent:
+    def __init__(self):
+        self.app = app
+
+    def __call__(self, question: str) -> str:
+        inputs = {"messages": [HumanMessage(content=question)]}
+        final_state = self.app.invoke(inputs)
+        return final_state['messages'][-1].content

app.py

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+import os
+import gradio as gr
+import requests
+import pandas as pd
+from dotenv import load_dotenv
+from agent import LangGraphAgent
+
+load_dotenv()
+
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the SimpleAgent 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 = LangGraphAgent()
+    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}")
+    response = None  # Initialize response to None
+    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}")
+        # Try to get more specific error information
+        if isinstance(e, requests.exceptions.ConnectionError):
+            return "Error fetching questions: Connection Error. Please check the API URL and your network connection.", None
+        if isinstance(e, requests.exceptions.Timeout):
+            return "Error fetching questions: Request timed out.", None
+        if response:
+             try:
+                  error_json = response.json()
+                  error_detail = error_json.get('detail', response.text)
+                  return f"Error fetching questions: {e} - {error_detail}", None
+             except requests.exceptions.JSONDecodeError:
+                  return f"Error fetching questions: {e} - Could not decode JSON from response: {response.text[:500]}", None
+        return f"Error fetching 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.strip()})
+            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
+
+
+def test_agent(question: str):
+    """
+    Runs the agent on a single question and returns the answer.
+    """
+    if not question:
+        return "Please enter a question."
+    try:
+        agent = LangGraphAgent()
+        answer = agent(question)
+        return answer
+    except Exception as e:
+        return f"Error running agent: {e}"
+
+# --- 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.
+        """
+    )
+
+    login_button = 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,
+        inputs=[login_button],
+        outputs=[status_output, results_table]
+    )
+
+
+    gr.Markdown("---")
+    gr.Markdown("## Test the Agent")
+    with gr.Row():
+        question_textbox = gr.Textbox(label="Enter your question")
+        answer_textbox = gr.Textbox(label="Agent's Answer")
+    test_button = gr.Button("Test Agent")
+
+    test_button.click(
+        fn=test_agent,
+        inputs=[question_textbox],
+        outputs=[answer_textbox]
+    )
+
+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("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("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)
+

requirements.txt

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+gradio[oauth]
+requests
+langchain
+langgraph
+python-dotenv
+langchain-community
+tavily-python
+langchain-google-genai
+google-auth
+langchain-tavily
+google-cloud-aiplatform