Update app.py

app.py

@@ -1,380 +1,379 @@
-# import os
-# import gradio as gr
-# import requests
-# import inspect
-# import pandas as pd
-
-# # Add this line with the other imports
-# from agent import BasicAgent
-
-# # (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.")
-#     def __call__(self, question: str) -> str:
-#         print(f"Agent received question (first 50 chars): {question[:50]}...")
-#         fixed_answer = "This is a default answer."
-#         print(f"Agent returning fixed answer: {fixed_answer}")
-#         return fixed_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)
-
-
 import os
 import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from dotenv import load_dotenv
-from typing import TypedDict, Annotated, List
-
-# ==============================================================================
-# PART 1: YOUR AGENT'S LOGIC AND DEFINITION
-# ==============================================================================
-
-# LangChain and LangGraph imports
-from langchain_huggingface import HuggingFaceEndpoint
-# NEW: Import TavilySearch from the new package
-from langchain_tavily import TavilySearch
-from langchain_experimental.tools import PythonREPLTool
-from langchain_core.messages import BaseMessage, HumanMessage
-from langgraph.graph import StateGraph, END
-from langgraph.prebuilt import ToolNode
-# NEW: Import the compatible agent constructor and prompt hub
-from langchain.agents import create_tool_calling_agent
-from langchain import hub
-
-
-# Load API keys from .env file or Space secrets
-load_dotenv()
-hf_token = os.getenv("HF_TOKEN")
-tavily_api_key = os.getenv("TAVILY_API_KEY")
-
-if tavily_api_key:
-    os.environ["TAVILY_API_KEY"] = tavily_api_key
-else:
-    print("Warning: TAVILY_API_KEY not found. Web search tool will not work.")
-
-# --- Define Agent Tools ---
-# NEW: Using TavilySearch from the correct package
-tools = [
-    TavilySearch(max_results=3, description="A search engine for finding up-to-date information on the web."),
-    PythonREPLTool()
-]
-tool_node = ToolNode(tools)
-
-# --- Configure the LLM "Brain" ---
-repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
-
-llm = HuggingFaceEndpoint(
-    repo_id=repo_id,
-    huggingfacehub_api_token=hf_token,
-    temperature=0,
-    max_new_tokens=2048,
-)
-
-# --- THE FIX: Create Agent with a Compatible Method ---
-# REMOVED: llm_with_tools = llm.bind_tools(tools)
-# This was causing the error.
-
-# NEW: We pull a pre-made prompt that knows how to handle tool calls.
-prompt = hub.pull("hwchase17/react-json")
-
-# NEW: We use `create_tool_calling_agent`. This function correctly combines the LLM,
-# the tools, and the prompt, without needing the .bind_tools() method.
-agent_runnable = create_tool_calling_agent(llm, tools, prompt)
-
-
-# --- Build the LangGraph Agent ---
-class AgentState(TypedDict):
-    # The 'messages' key is no longer used, 'input' and 'agent_outcome' are standard for this agent type
-    input: str
-    chat_history: list[BaseMessage]
-    agent_outcome: dict
-
-# NEW: The agent_node is much simpler now. It just calls the runnable we created.
-def agent_node(state):
-    outcome = agent_runnable.invoke(state)
-    return {"agent_outcome": outcome}
-
-def tool_node_executor(state):
-    # The agent_runnable provides tool calls in a specific format. We execute them.
-    tool_calls = state["agent_outcome"].tool_calls
-    tool_outputs = []
-    for tool_call in tool_calls:
-        tool_name = tool_call["name"]
-        tool_to_call = {tool.name: tool for tool in tools}[tool_name]
-        tool_output = tool_to_call.invoke(tool_call["args"])
-        tool_outputs.append({"output": tool_output, "tool_call_id": tool_call["id"]})
-    return {"intermediate_steps": tool_outputs}
-
-
-# This setup is more complex but correctly models the ReAct loop in LangGraph
-class BasicAgent:
-    def __init__(self):
-        if not hf_token or not tavily_api_key:
-            raise ValueError("HF_TOKEN or TAVILY_API_KEY not set. Please add them to your Space secrets.")
-        print("LangGraph Agent initialized successfully.")
-        # We need an agent executor to run the loop
-        from langchain.agents import AgentExecutor
-        self.agent_executor = AgentExecutor(agent=agent_runnable, tools=tools, verbose=True)
 
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 80 chars): {question[:80]}...")
-        try:
-            # The AgentExecutor expects a dictionary with an "input" key.
-            response = self.agent_executor.invoke({"input": question})
-            final_answer = response.get("output", "Agent did not produce an output.")
-            print(f"Agent returning final answer (first 80 chars): {final_answer[:80]}...")
-            return final_answer
-        except Exception as e:
-            print(f"An error occurred in agent execution: {e}")
-            return f"Error: {e}"
 
 
-# ==============================================================================
-# PART 2: THE GRADIO TEST HARNESS UI (UNCHANGED)
-# ==============================================================================
+# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    # This entire function remains the same as the template
-    space_id = os.getenv("SPACE_ID")
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
+    def __init__(self):
+        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."
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_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
-    results_log, answers_payload = [], []
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
-        task_id, question_text = item.get("task_id"), item.get("question")
-        if not task_id or question_text is None: continue
+        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:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-    if not answers_payload: return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+             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!\nUser: {result_data.get('username')}\nOverall Score: {result_data.get('score', 'N/A')}% ({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\nMessage: {result_data.get('message', '')}")
-        return final_status, pd.DataFrame(results_log)
+        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:
-        return f"An unexpected error occurred during submission: {e}", pd.DataFrame(results_log)
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
 
-# --- Gradio Interface (Unchanged) ---
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Agent Evaluation Runner")
-    gr.Markdown("1. Log in. 2. Click 'Run Evaluation'.")
+    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])
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    demo.launch(debug=True, share=False)
+    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)
+
+###########################################
+# import os
+# import gradio as gr
+# import requests
+# import inspect
+# import pandas as pd
+# from dotenv import load_dotenv
+# from typing import TypedDict, Annotated, List
+
+# # ==============================================================================
+# # PART 1: YOUR AGENT'S LOGIC AND DEFINITION
+# # ==============================================================================
+
+# # LangChain and LangGraph imports
+# from langchain_huggingface import HuggingFaceEndpoint
+# # NEW: Import TavilySearch from the new package
+# from langchain_tavily import TavilySearch
+# from langchain_experimental.tools import PythonREPLTool
+# from langchain_core.messages import BaseMessage, HumanMessage
+# from langgraph.graph import StateGraph, END
+# from langgraph.prebuilt import ToolNode
+# # NEW: Import the compatible agent constructor and prompt hub
+# from langchain.agents import create_tool_calling_agent
+# from langchain import hub
+
+
+# # Load API keys from .env file or Space secrets
+# load_dotenv()
+# hf_token = os.getenv("HF_TOKEN")
+# tavily_api_key = os.getenv("TAVILY_API_KEY")
+
+# if tavily_api_key:
+#     os.environ["TAVILY_API_KEY"] = tavily_api_key
+# else:
+#     print("Warning: TAVILY_API_KEY not found. Web search tool will not work.")
+
+# # --- Define Agent Tools ---
+# # NEW: Using TavilySearch from the correct package
+# tools = [
+#     TavilySearch(max_results=3, description="A search engine for finding up-to-date information on the web."),
+#     PythonREPLTool()
+# ]
+# tool_node = ToolNode(tools)
+
+# # --- Configure the LLM "Brain" ---
+# repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+# llm = HuggingFaceEndpoint(
+#     repo_id=repo_id,
+#     huggingfacehub_api_token=hf_token,
+#     temperature=0,
+#     max_new_tokens=2048,
+# )
+
+# # --- THE FIX: Create Agent with a Compatible Method ---
+# # REMOVED: llm_with_tools = llm.bind_tools(tools)
+# # This was causing the error.
+
+# # NEW: We pull a pre-made prompt that knows how to handle tool calls.
+# prompt = hub.pull("hwchase17/react-json")
+
+# # NEW: We use `create_tool_calling_agent`. This function correctly combines the LLM,
+# # the tools, and the prompt, without needing the .bind_tools() method.
+# agent_runnable = create_tool_calling_agent(llm, tools, prompt)
+
+
+# # --- Build the LangGraph Agent ---
+# class AgentState(TypedDict):
+#     # The 'messages' key is no longer used, 'input' and 'agent_outcome' are standard for this agent type
+#     input: str
+#     chat_history: list[BaseMessage]
+#     agent_outcome: dict
+
+# # NEW: The agent_node is much simpler now. It just calls the runnable we created.
+# def agent_node(state):
+#     outcome = agent_runnable.invoke(state)
+#     return {"agent_outcome": outcome}
+
+# def tool_node_executor(state):
+#     # The agent_runnable provides tool calls in a specific format. We execute them.
+#     tool_calls = state["agent_outcome"].tool_calls
+#     tool_outputs = []
+#     for tool_call in tool_calls:
+#         tool_name = tool_call["name"]
+#         tool_to_call = {tool.name: tool for tool in tools}[tool_name]
+#         tool_output = tool_to_call.invoke(tool_call["args"])
+#         tool_outputs.append({"output": tool_output, "tool_call_id": tool_call["id"]})
+#     return {"intermediate_steps": tool_outputs}
+
+
+# # This setup is more complex but correctly models the ReAct loop in LangGraph
+# class BasicAgent:
+#     def __init__(self):
+#         if not hf_token or not tavily_api_key:
+#             raise ValueError("HF_TOKEN or TAVILY_API_KEY not set. Please add them to your Space secrets.")
+#         print("LangGraph Agent initialized successfully.")
+#         # We need an agent executor to run the loop
+#         from langchain.agents import AgentExecutor
+#         self.agent_executor = AgentExecutor(agent=agent_runnable, tools=tools, verbose=True)
+
+#     def __call__(self, question: str) -> str:
+#         print(f"Agent received question (first 80 chars): {question[:80]}...")
+#         try:
+#             # The AgentExecutor expects a dictionary with an "input" key.
+#             response = self.agent_executor.invoke({"input": question})
+#             final_answer = response.get("output", "Agent did not produce an output.")
+#             print(f"Agent returning final answer (first 80 chars): {final_answer[:80]}...")
+#             return final_answer
+#         except Exception as e:
+#             print(f"An error occurred in agent execution: {e}")
+#             return f"Error: {e}"
+
+
+# # ==============================================================================
+# # PART 2: THE GRADIO TEST HARNESS UI (UNCHANGED)
+# # ==============================================================================
+# # --- Constants ---
+# DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# def run_and_submit_all(profile: gr.OAuthProfile | None):
+#     # This entire function remains the same as the template
+#     space_id = os.getenv("SPACE_ID")
+#     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"
+#     try:
+#         agent = BasicAgent()
+#     except Exception as e:
+#         print(f"Error instantiating agent: {e}")
+#         return f"Error initializing agent: {e}", None
+#     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+#     print(f"Fetching questions from: {questions_url}")
+#     try:
+#         response = requests.get(questions_url, timeout=15)
+#         response.raise_for_status()
+#         questions_data = response.json()
+#         print(f"Fetched {len(questions_data)} questions.")
+#     except Exception as e:
+#         return f"An unexpected error occurred fetching questions: {e}", None
+#     results_log, answers_payload = [], []
+#     print(f"Running agent on {len(questions_data)} questions...")
+#     for item in questions_data:
+#         task_id, question_text = item.get("task_id"), item.get("question")
+#         if not task_id or question_text is None: 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:
+#             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+#     if not answers_payload: return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+#     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+#     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!\nUser: {result_data.get('username')}\nOverall Score: {result_data.get('score', 'N/A')}% ({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\nMessage: {result_data.get('message', '')}")
+#         return final_status, pd.DataFrame(results_log)
+#     except Exception as e:
+#         return f"An unexpected error occurred during submission: {e}", pd.DataFrame(results_log)
+
+# # --- Gradio Interface (Unchanged) ---
+# with gr.Blocks() as demo:
+#     gr.Markdown("# GAIA Agent Evaluation Runner")
+#     gr.Markdown("1. Log in. 2. Click 'Run Evaluation'.")
+#     gr.LoginButton()
+#     run_button = gr.Button("Run Evaluation & Submit All Answers")
+#     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+#     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__":
+#     demo.launch(debug=True, share=False)