app.py

"""
NOTE:
    - If USE_RATE_LIMITER env variable is True, the agent will use a rate limiter to avoid hitting API limits.
"""

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from agent import build_agent
from langchain_core.messages import HumanMessage
from langfuse.langchain import CallbackHandler

langfuse_handler = CallbackHandler()

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
questions_url = f"{DEFAULT_API_URL}/questions"
submit_url = f"{DEFAULT_API_URL}/submit"
files_url = f"{DEFAULT_API_URL}/files/" # Needs task_id

# --- Basic Agent Definition ---
class SuperAgent:
    def __init__(self):
        print("SuperAgent initialized.")
        self.agent = build_agent(provider="google")  # Change to "hf" for HuggingFace
        self.recursion_limit = os.getenv("RECURSION_LIMIT", "25")

    def __call__(self, data: dict) -> str:
        """
        Args:
            data (str): A string containing the question to be answered.
                Schema: {
                    task_id: str,
                    question: str,
                    file_name: str,
                }
        """
        # Quick validation of input data (TODO: Use pydantic for schema)
        required_keys = ["question", "task_id", "file_name"]
        if not all(k in data for k in required_keys):
            raise ValueError("Input data must contain 'question', 'task_id', and 'file_name'.")
        
        task_id, question, file_name = data["task_id"], data["question"], data["file_name"]
        
        print(f"Agent received question (first 50 chars): {question[:50]}...")

        # Build HumanMessage
        content = [
            {"type": "text", "text": question}
        ]
        
        if file_name != "":
            file_url = f"{files_url}{task_id}"
        
            if file_name.endswith((".png", ".jpg", ".jpeg")):
                content.append({"type": "image_url", "image_url": {"url": file_url}})

            elif file_name.endswith((".py")):
                # For code files, we can just send the text content
                try:
                    response = requests.get(file_url, timeout=15)
                    response.raise_for_status()
                    code_content = response.text

                    content.append({"type": "text", "text": code_content})
                except Exception as e:
                    print(f"Error fetching code file: {e}")

            elif file_name.endswith((".xlsx", ".xls")):
                content.append({"type": "text", "text": "Excel file url: " + file_url})
            
            elif file_name.endswith((".mp3", ".wav")):
                content.append({"type": "text", "text": "Audio file url: " + file_url})

            else:
                raise ValueError(f"Unsupported file type for file: {file_name}")
        
        human_msg = HumanMessage(content=content)

        try:
            answer = self.agent.invoke(
                {"messages": [human_msg]},
                config={"callbacks": [langfuse_handler], "recursion_limit": self.recursion_limit}
            )

            # for message in answer["messages"]:
            #     message.pretty_print()
            # Result already printed inside assistant() node
        except Exception as e:
            print(f"Error: {e}")

        return answer["messages"][-1].content



def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the SuperAgent 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

    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = SuperAgent()
    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

    # Read excluded task IDs from file
    excluded_tasks = set()
    with open("excluded_tasks.txt", "r") as f:
        for line in f:
            task_id = line.strip()
            if task_id:
                excluded_tasks.add(task_id)

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    for idx, item in enumerate(questions_data):
        task_id = item.get("task_id")
        question_text = item.get("question")
        
        print(f"[{idx+1}/{len(questions_data)}]", end=" ")

        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue

        # Skip excluded tasks
        if task_id in excluded_tasks:
            print(f"Skipping excluded task: {task_id}")
            continue

        try:
            submitted_answer = agent(item)

            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})

            # Print the answer for debugging
            # print the timestamp
            print(f"Task ID: {task_id}, Submitted Answer: {submitted_answer[:50]}")
        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

# Read the app description from markdown file
with open("description.md", "r", encoding="utf-8") as f:
    description_md = f.read()

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Super Agent Evaluation Runner")
    gr.Markdown(description_md)

    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=True)