app.py

# 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):
#         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)

##################################
#
# =================================================================================================
#  ✅ --- ✅  FINAL ASSESSMENT AGENT - INSTRUCTOR'S VERSION ✅ --- ✅
# =================================================================================================
#
#  Instructions:
#  1. Make sure you have a requirements.txt file with all the necessary packages.
#  2. Set your GROQ_API_KEY in the Hugging Face Space secrets.
#  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 pandas as pd
import gradio as gr
from contextlib import redirect_stdout
from typing import TypedDict, Annotated, List
import operator

# --- LangChain & LangGraph Imports ---
from langchain_core.messages import BaseMessage, HumanMessage, ToolMessage, AIMessage
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 ToolNode # <-- Corrected Import for modern LangGraph

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
FILES_DIR = "./files"
os.makedirs(FILES_DIR, exist_ok=True)

#
# ================================================================================================
#  ✅ 1. DEFINE THE AGENT'S TOOLS
# ================================================================================================
#  Each tool is a simple Python function decorated with `@tool`.
#  The docstring of the function is CRUCIAL. The LLM uses it to decide which tool to use.
#

@tool
def web_search(query: str) -> str:
    """
    Searches the web using DuckDuckGo to find up-to-date information, facts, or answers to general questions.
    Use this for any questions that require current event knowledge or broad-spectrum information.
    """
    print(f"--- Calling Web Search Tool with query: {query} ---")
    from duckduckgo_search import DDGS
    try:
        with DDGS() as ddgs:
            results = [r for r in ddgs.text(query, max_results=5)]
            return str(results) if results else "No results found."
    except Exception as e:
        return f"Error during web search: {e}"

@tool
def read_file(url: str) -> str:
    """
    Downloads a file from a given URL, saves it locally, and returns its content.
    Use this tool when the user provides a URL to a file that needs to be inspected.
    The file is saved in the './files/' directory. The function returns the full text content.
    """
    print(f"--- Calling Read File Tool with URL: {url} ---")
    try:
        filename = os.path.join(FILES_DIR, os.path.basename(url))
        response = requests.get(url)
        response.raise_for_status()  # Raise an exception for bad status codes
        with open(filename, 'wb') as f:
            f.write(response.content)
        
        # Try to read as text, if it fails, it might be a binary file.
        try:
            with open(filename, 'r', encoding='utf-8') as f:
                content = f.read()
            return f"Successfully read file '{filename}'. Content:\n\n{content}"
        except UnicodeDecodeError:
            return f"Successfully downloaded binary file '{filename}'. Cannot display content."
            
    except requests.exceptions.RequestException as e:
        return f"Error downloading or reading file: {e}"

@tool
def python_interpreter(code: str) -> str:
    """
    Executes a given string of Python code and returns the output from stdout.
    Use this for complex calculations, data manipulation, or any task that can be solved with code.
    The code runs in a restricted environment. You can use libraries like pandas, requests etc.
    Make sure to use a print() statement to capture the output.
    """
    print(f"--- Calling Python Interpreter Tool with code:\n{code} ---")
    output_buffer = io.StringIO()
    try:
        with redirect_stdout(output_buffer):
            exec(code, globals())
        return f"Code executed successfully. Output:\n{output_buffer.getvalue()}"
    except Exception as e:
        return f"Error executing Python code: {e}"

#
# ================================================================================================
#  ✅ 2. CONFIGURE THE AGENT'S STATE, BRAIN (LLM)
# ================================================================================================
#

# The AgentState is the "memory" of our agent. It keeps track of the conversation history.
class AgentState(TypedDict):
    messages: Annotated[List[BaseMessage], operator.add]

# List of all the tools our agent can use
tools = [web_search, read_file, python_interpreter]

# 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)

# If you want to use OpenAI instead, uncomment the line below and set OPENAI_API_KEY
# llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)

# We now bind the tools to the LLM. This tells the LLM what functions it can call.
llm_with_tools = llm.bind_tools(tools)

#
# ================================================================================================
#  ✅ 3. DEFINE THE LANGGRAPH NODES AND EDGES
# ================================================================================================
#  This is the core logic of our agent, defined as a graph.
#

# NODE 1: The Agent Node (call_model)
# This node invokes the LLM to decide the next action or to give a final answer.
def call_model(state: AgentState) -> dict:
    print("--- Calling LLM ---")
    messages = state['messages']
    response = llm_with_tools.invoke(messages)
    # We return a dict, because this node will always be part of a graph
    return {"messages": [response]}

# 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 to execute the tool
    if last_message.tool_calls:
        print("--- Decision: Call a tool ---")
        return "action"
    # Otherwise, we are done, and we route to the 'end' state
    else:
        print("--- Decision: End of process ---")
        return "end"

#
# ================================================================================================
#  ✅ 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 need: the 'agent' and the 'action' (our tool_node)
workflow.add_node("agent", call_model)
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")

# 4. Add the conditional edge. This controls the flow of the graph.
workflow.add_conditional_edges(
    "agent",          # Start from the 'agent' node
    should_continue,  # Use our function to decide the path
    {
        "action": "action", # If it returns "action", go to the 'action' node
        "end": END          # If it returns "end", finish the graph
    }
)

# 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.
app = workflow.compile()


#
# ================================================================================================
#  ✅ 5. CREATE THE AGENT CLASS THAT THE TEMPLATE USES
# ================================================================================================
#  This class wraps our LangGraph agent in the format expected by the evaluation script.
#
class GaiaAgent:
    def __init__(self):
        print("GaiaAgent initialized.")
        self.agent_app = app

    def __call__(self, question: str) -> str:
        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": 15})):
            if i == 0:
                print("--- Starting Agentic Loop ---")
            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"\n--- Agent finished. Final Answer: {final_answer} ---\n")
        return final_answer

#
# ================================================================================================
#  -- DO NOT MODIFY THE CODE BELOW THIS LINE --
#  -- This is the Gradio App and Submission Logic from the course --
# ================================================================================================

def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    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 = GaiaAgent()
    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(agent_code)

    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 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 = []
    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)

    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)

    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("# GAIA Agent Final Assessment")
    gr.Markdown(
        """
        **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 with the specified versions.
        3.  Log in below and click 'Run Evaluation'. Good luck!
        """
    )

    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__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    space_id_startup = os.getenv("SPACE_ID")
    if space_id_startup:
        print(f"✅ SPACE_ID found: {space_id_startup}")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?).")
    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for GAIA Agent Evaluation...")
    demo.launch(debug=True, share=False)