app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from langgraph.prebuilt import ToolNode, tools_condition
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, HumanMessage
from langgraph.graph import START, StateGraph
from langchain_google_genai import ChatGoogleGenerativeAI
from ToolSet import toolset
from utils.final_answer import extract_final_answer
from utils.handle_file import handle_attachment
from fetch_question import get_all_questions, get_one_random_question, submit

# (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 ------
gemini_api_key = os.getenv("GEMINI_API_KEY")
tivaly_api_key = os.getenv("TAVILY_API_KEY")

llm = ChatGoogleGenerativeAI(
    model="gemini-2.0-flash",
    temperature=0,
    google_api_key = gemini_api_key
)

llm_with_tools = llm.bind_tools(toolset)

sys_prompt_file = open("sys_prompt.txt")
sys_prompt = sys_prompt_file.read()

class AgentState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]

def assistant(state: AgentState):
    return {
        "messages": [llm_with_tools.invoke([sys_prompt]+state["messages"])],
    }

builder = StateGraph(AgentState)

builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(available_tools))

builder.add_edge(START, "assistant")
builder.add_conditional_edges(
    "assistant",
    tools_condition
)
builder.add_edge("tools","assistant")

gaia_agent = builder.compile()

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the agent on them, submits all answers,
    and displays the results. Handles attachments if present.
    """
    # --- 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
    
    # 1. Instantiate Agent (modify this part to create your agent)
    try:
        agent = my_agent
    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 (useful for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)
    
    # 2. Fetch Questions
    questions_data = get_all_questions()
    
    # 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
        
        # 2.2 Handle attachment if present
        attachment_info = None
        if "file_name" in item and item["file_name"]:
            file_name = item.get("file_name")
            attachment_info = handle_attachment(task_id, file_name)
            print(f"Attachment handling result: {attachment_info['status']}")
        
        try:
            # Prepare messages based on attachment handling
            messages = [
                SystemMessage(content=SYSTEM_PROMPT),
                SystemMessage(content=f"Current task id: {task_id}")
            ]
            
            # If we have an attachment that Claude can process directly
            if attachment_info and attachment_info["status"] == "success" and attachment_info["handling"] == "direct":
                # Encode content for direct inclusion
                encoded_content = base64.b64encode(attachment_info["raw_content"]).decode('utf-8')
                content_type = attachment_info["content_type"]
                
                # Create multimodal message
                if content_type.startswith('image/'):
                    multimodal_content = [
                        {"type": "text", "text": question_text},
                        {
                            "type": "image", 
                            "source": {
                                "type": "base64", 
                                "media_type": content_type,
                                "data": encoded_content
                            }
                        }
                    ]
                elif content_type == "application/pdf" or "spreadsheet" in content_type or "excel" in content_type or "csv" in content_type:
                    multimodal_content = [
                        {"type": "text", "text": question_text},
                        {
                            "type": "file", 
                            "source": {
                                "type": "base64", 
                                "media_type": content_type,
                                "data": encoded_content
                            },
                            "name": attachment_info["file_name"]
                        }
                    ]
                
                messages.append(HumanMessage(content=multimodal_content))
                
            # If we have an attachment that needs tool processing
            elif attachment_info and attachment_info["status"] == "success" and attachment_info["handling"] == "tool":
                # Add info about the file to the question
                file_info = (
                    f"{question_text}\n\n"
                    f"Note: This task has an attached file that can be accessed at: {attachment_info['file_path']}\n"
                    f"File type: {attachment_info['content_type']}"
                )
                messages.append(HumanMessage(content=file_info))
                
            # If no attachment or error with attachment
            else:
                messages.append(HumanMessage(content=question_text))
            
            # Invoke the agent with the prepared messages
            agent_answer = agent.invoke({"messages": messages},{"recursion_limit": 50})
            submitted_answer = extract_final_answer(agent_answer['messages'][-1].content)
            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
    return submit(submission_data, results_log)

def run_and_submit_one( profile: gr.OAuthProfile | None):
    # --- 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
    
    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = my_agent
    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
    questions_data = get_one_random_question()
    print("questions_data:", questions_data)
    
    # 2.2 Handle attachment if present
    attachment_info = None
    if "file_name" in questions_data and questions_data["file_name"]:
        task_id = questions_data.get("task_id")
        file_name = questions_data.get("file_name")
        attachment_info = handle_attachment(task_id, file_name)
        print(f"Attachment handling result: {attachment_info['status']}")

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")

    task_id = questions_data.get("task_id")
    question_text = questions_data.get("question")

    if not task_id or question_text is None:
        print(f"Skipping item with missing task_id or question")
    try:
            # Prepare messages based on attachment handling
            messages = [
                SystemMessage(content=SYSTEM_PROMPT),
                SystemMessage(content=f"Current task id: {task_id}")
            ]
            
            # If we have an attachment that Claude can process directly
            if attachment_info and attachment_info["status"] == "success" and attachment_info["handling"] == "direct":
                # Encode content for direct inclusion
                encoded_content = base64.b64encode(attachment_info["raw_content"]).decode('utf-8')
                content_type = attachment_info["content_type"]
                
                # Create multimodal message
                if content_type.startswith('image/'):
                    multimodal_content = [
                        {"type": "text", "text": question_text},
                        {
                            "type": "image", 
                            "source": {
                                "type": "base64", 
                                "media_type": content_type,
                                "data": encoded_content
                            }
                        }
                    ]
                elif content_type == "application/pdf" or "spreadsheet" in content_type or "excel" in content_type or "csv" in content_type:
                    multimodal_content = [
                        {"type": "text", "text": question_text},
                        {
                            "type": "file", 
                            "source": {
                                "type": "base64", 
                                "media_type": content_type,
                                "data": encoded_content
                            },
                            "name": attachment_info["file_name"]
                        }
                    ]
                
                messages.append(HumanMessage(content=multimodal_content))
                
            # If we have an attachment that needs tool processing
            elif attachment_info and attachment_info["status"] == "success" and attachment_info["handling"] == "tool":
                # Add info about the file to the question
                file_info = (
                    f"{question_text}\n\n"
                    f"Note: This task has an attached file that can be accessed at: {attachment_info['file_path']}\n"
                    f"File type: {attachment_info['content_type']}"
                )
                messages.append(HumanMessage(content=file_info))
                
            # If no attachment or error with attachment
            else:
                messages.append(HumanMessage(content=question_text))
            
            # Invoke the agent with the prepared messages
            agent_answer = agent.invoke({"messages": messages},{"recursion_limit": 50})
            submitted_answer = extract_final_answer(agent_answer['messages'][-1].content)
            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)

    return submit(submission_data, results_log)

# --- 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")
    run_one_button = gr.Button("Run one question and submit")

    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_one_button.click(
        fn=run_and_submit_one,
        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)