app.py

# app.py  —  fully updated to avoid the 32 768-token ceiling
# -----------------------------------------------------------------------------
# Changes vs. the original paste.txt [1] follow the “### CHANGE” comments.
# Core idea taken from the fresh-agent pattern shown in result [2].

import os
import gradio as gr
import requests
import inspect
import pandas as pd

# Tools -----------------------------------------------------------------------
from tools import (
    ReverseTextTool,
    RunPythonFileTool,
    download_server,
    wiki_tool,
    YoutubeTranscript,
)
from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec


# Llama-Index / HF Inference ---------------------------------------------------
from llama_index.core.agent.workflow import AgentWorkflow           # [1]
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI  # [1]

# --- Constants ---------------------------------------------------------------
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question.
Report your thoughts, and finish your answer with just the answer — no prefixes like "FINAL ANSWER:".
Your answer should be a number OR as few words as possible OR a comma-separated list of numbers and/or strings.
If you're asked for a number, don’t use commas or units like $ or %, unless specified.
If you're asked for a string, don’t use articles or abbreviations (e.g. for cities), and write digits in plain text unless told otherwise.
Always create a detailed thinking and reasoning plan before taking an action.
If reasoning in previous step didn't get you correct answer, try to use other reasoning but it must achieve the same outcome.

Tool Use Guidelines:
1. Do **not** use any tools outside of the provided tools list.
2. Always use **only one tool at a time** in each step of your execution.
3. If the question refers to a `.py` file or uploaded Python script, use **RunPythonFileTool** to execute it and base your answer on its output.
4. If the question looks reversed (starts with a period or reads backward), first use **ReverseTextTool** to reverse it, then process the question.
5. For logic or word puzzles, solve them directly unless they are reversed — in which case, decode first using **ReverseTextTool**.
6. When dealing with Excel files, prioritize using the **excel** tool over writing code in **terminal-controller**.
7. If you need to download a file, always use the **download_server** tool and save it to the correct path.
8. To find information about what was said in a video use **YoutubeTranscript** tool to get the transcript and find the correct answer to the asked question
9. Even for complex tasks, assume a solution exists. If one method fails, try another approach using different tools.
10. Due to context length limits, keep browser-based tasks (e.g., searches) as short and efficient as possible.
11. Use DuckDuckGoSearchToolSpec to find or verify information from web search.
12. Use wiki_tool to find answers from wikipedia.
"""

# -----------------------------------------------------------------------------
# BasicAgent – spawns a brand-new AgentWorkflow for *each* question  [2]
# -----------------------------------------------------------------------------
class BasicAgent:
    """LLM + tool set kept once; AgentWorkflow rebuilt per question."""

    def __init__(self) -> None:
        hf_api_key = os.getenv("HF_API_KEY")
        if not hf_api_key:
            raise RuntimeError("HF_API_KEY not set in environment variables.")

        # single, stateless LLM reused across questions
        self.llm = HuggingFaceInferenceAPI(
            model_name="Qwen/Qwen2.5-Coder-32B-Instruct",
            token=hf_api_key,
            max_tokens=256,                   # output length only
        )

        self._tools = [
            ReverseTextTool,
            RunPythonFileTool,
            download_server,
            wiki_tool,
            YoutubeTranscript,
            DuckDuckGoSearchToolSpec,
        ]
        print("✅ BasicAgent initialized.")

    # ---------- internal helper ---------------------------------------------
    def _make_agent(self) -> AgentWorkflow:
        """Return a FRESH AgentWorkflow with empty context."""
        return AgentWorkflow.from_tools_or_functions(
            tools_or_functions=self._tools,
            llm=self.llm,
            system_prompt=SYSTEM_PROMPT,
        )
        print("✅ BasicAgent initialized.")

    # ---------- public helpers ----------------------------------------------
    async def answer_once(self, prompt: str) -> str:
        """Answer one question while guaranteeing ctx < 32 768 tokens."""
        MAX_IN_TOKENS = 30000                 # ~2-3 k room for system + tools
        prompt = prompt[:MAX_IN_TOKENS]       # naïve clip by characters

        agent = self._make_agent()            # NO prior history
        resp  = await agent.run(prompt)
        return resp if isinstance(resp, str) else str(resp)

    # keep backwards-compat method names
    async def __call__(self, input_text: str):
        return await self.answer_once(input_text)

    async def run(self, input_text: str):
        return await self.answer_once(input_text)

    async def stream(self, input_text: str):
        agent = self._make_agent()
        async for chunk in agent.stream(input_text):
            yield chunk.delta

# -----------------------------------------------------------------------------
# Evaluation / submission logic (unchanged except per-question call)  [1] [2]
# -----------------------------------------------------------------------------
async def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetch questions, answer them one-by-one with BasicAgent, then submit.
    """
    # --- user / URLs ---------------------------------------------------------
    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"

    # --- instantiate agent ---------------------------------------------------
    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(agent_code)

    # --- fetch questions -----------------------------------------------------
    print(f"Fetching questions from: {questions_url}")
    try:
        resp = requests.get(questions_url, timeout=15)
        resp.raise_for_status()
        questions_data = resp.json()
        if not questions_data:
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # --- answer questions one-by-one ----------------------------------------
    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 = await agent(question_text)      # <<< one call
            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)