app.py

import os
import re
import sys
import time
import concurrent.futures
# Force UTF-8 output on Windows to avoid charmap crashes with Unicode characters
if sys.platform == "win32":
    sys.stdout.reconfigure(encoding="utf-8", errors="replace")
    sys.stderr.reconfigure(encoding="utf-8", errors="replace")
import gradio as gr
import requests
import pandas as pd
from typing import Literal, TypedDict, get_args
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph
from config import DEFAULT_API_URL, HF_TOKEN, GROQ_API_KEY, OPENROUTER_API_KEY, get_prompt
from tools import (
    web_search,
    wikipedia_search,
    visit_webpage,
    get_youtube_transcript,
    describe_image,
    transcribe_audio,
    run_python_file,
    read_task_file,
)

# ---------------------------------------------------------------------------
# Model fallback chain (primary → backup → last-resort)
# ---------------------------------------------------------------------------
# Use OpenRouter for the main reasoning model (better quality) and Groq for routing (fast)
GROQ_MODELS = [
    {"model_id": "llama-3.3-70b-versatile"},
    {"model_id": "llama-3.1-8b-instant"},
]

OPENROUTER_MODELS = [
    {"model_id": "google/gemini-2.0-flash-001"},
    {"model_id": "qwen/qwen-2.5-72b-instruct"},
    {"model_id": "meta-llama/llama-3.3-70b-instruct"},
]

_LABELS = Literal[
    "python_script",
    "image",
    "audio",
    "other_ext",
    "youtube",
    "research",
    "logic"
]

def _download_task_file(task_id: str, api_url: str = DEFAULT_API_URL) -> tuple[bytes, str]:
    """Download a file attached to a GAIA task."""
    url = f"{api_url}/files/{task_id}"
    try:
        headers = {"Authorization": f"Bearer {HF_TOKEN}"}
        resp = requests.get(url, headers=headers, timeout=30)
    except requests.exceptions.RequestException as e:
        print(f"[DEBUG] Download error for {task_id}: {e}")
        return b"", ""
    if resp.status_code != 200:
        print(f"[DEBUG] GET {url} → {resp.status_code}")
        return b"", ""
    ctype = resp.headers.get("content-type", "").lower()
    print(f"[DEBUG] Downloaded file for {task_id}: {len(resp.content)} bytes, type={ctype}")
    return resp.content, ctype

class AgentState(TypedDict):
    question: str
    label: str
    context: str
    answer: str
    task_id: str | None
    file_name: str | None


MAX_WORKERS = 1        # sequential to stay within rate limits
QUESTION_TIMEOUT = 300  # seconds before a single question is abandoned
_exhausted_models: set[str] = set()

# --------------------------------------------------------------------------- #
#                         NODES  (LangGraph  functions)                        #
# --------------------------------------------------------------------------- #
# Router uses Groq (fast, cheap)
_llm_router = ChatOpenAI(
    model=GROQ_MODELS[0]["model_id"],
    base_url="https://api.groq.com/openai/v1",
    api_key=GROQ_API_KEY,
    timeout=60,
)

# Reasoning uses OpenRouter (higher quality)
_llm_answer = ChatOpenAI(
    model=OPENROUTER_MODELS[0]["model_id"],
    base_url="https://openrouter.ai/api/v1",
    api_key=OPENROUTER_API_KEY,
    timeout=120,
)

def route_question(state: AgentState) -> AgentState:
    """Label the task so we know which toolchain to invoke."""
    question = state["question"]

    label_values = set(get_args(_LABELS))
    prompt = get_prompt(
        prompt_key="router",
        question=question,
        labels=", ".join(repr(v) for v in label_values),
    )
    resp = _llm_router.invoke(prompt).content.strip().lower()
    state["label"] = resp if resp in label_values else "logic"
    return state

def call_tools(state: AgentState) -> AgentState:
    question, label, task_id = state["question"], state["label"], state["task_id"]
    file_name = state.get("file_name") or ""

    matched_obj = re.search(r"https?://\S+", question)

    # ---- attachment (only when a file is actually attached to this task) -----
    if task_id and file_name:
        blob, ctype = _download_task_file(api_url=DEFAULT_API_URL, task_id=task_id)
        if blob:
            print(f"[DEBUG] attachment type={ctype}, size={len(blob)} bytes")
            if "python" in ctype or file_name.endswith(".py"):
                print("[DEBUG] Working with a Python attachment file")
                state["answer"] = run_python_file.invoke({"code": blob.decode("utf-8", errors="replace")})
                state["label"] = "python_script"
                return state
            if "audio" in ctype or any(file_name.endswith(ext) for ext in [".mp3", ".wav", ".m4a", ".flac"]):
                print("[DEBUG] Working with an audio attachment file")
                state["context"] = transcribe_audio.invoke({"audio_bytes": blob})
                state["label"] = "audio"
                return state
            if "image" in ctype or any(file_name.endswith(ext) for ext in [".png", ".jpg", ".jpeg", ".gif", ".webp"]):
                print("[DEBUG] Working with an image attachment file")
                state["answer"] = describe_image.invoke({"img_bytes": blob, "question": question})
                state["label"] = "image"
                return state
            # Excel / CSV / PDF / other binary
            print("[DEBUG] Working with a data file attachment")
            state["context"] = read_task_file.invoke({"xls_bytes": blob})
            state["label"] = "other_ext"
            return state

    # ---- label-based routing (when no file was fetched) ----------
    if label == "youtube":
        print("[TOOL] youtube_transcript")
        if matched_obj:
            url = re.sub(r'[.,;:!?")\]]+$', '', matched_obj.group(0))
            print(f"[TOOL] fetching transcript for: {url}")
            transcript = get_youtube_transcript.invoke({"video_url": url})
            if transcript and transcript != "TRANSCRIPT_UNAVAILABLE":
                state["context"] = transcript
            else:
                # Fallback: search for info about the video
                print("[TOOL] Transcript unavailable — searching web for video info")
                search_json = web_search.invoke({"query": f"youtube {url} transcript content"})
                state["context"] = f"TRANSCRIPT_UNAVAILABLE. Web search results about the video:\n{search_json}"
        else:
            print("[TOOL] youtube label but no URL found — falling back to web search")
            state["context"] = web_search.invoke({"query": question})

    elif label == "research":
        print("[TOOL] research — multi-step search")
        # Step 1: Generate a focused search query
        search_query_prompt = (
            "Write a short, precise search query (max 10 words) to answer this question. "
            "Include key proper nouns, dates, and specific terms. "
            "Output ONLY the query, nothing else.\n\nQuestion: " + question
        )
        focused_query = _llm_router.invoke(search_query_prompt).content.strip().strip('"').strip("'")
        print(f"[TOOL] search query: {focused_query}")

        # Step 2: Run web search + Wikipedia in parallel
        search_json = web_search.invoke({"query": focused_query})
        wiki_text = wikipedia_search.invoke({"query": focused_query})

        context_parts = []

        # Step 3: Visit top search result URLs to get full page content
        if search_json and search_json != "No search results found.":
            context_parts.append(f"WEB SEARCH RESULTS:\n{search_json}")
            try:
                import json as _json
                hits = _json.loads(search_json)
                # Visit top 2 result URLs for detailed content
                visited = 0
                for hit in hits[:4]:
                    link = hit.get("link", "")
                    if link and visited < 2:
                        page_content = visit_webpage.invoke({"url": link})
                        if page_content and "Could not fetch" not in page_content:
                            context_parts.append(f"\nPAGE CONTENT ({link}):\n{page_content[:15000]}")
                            visited += 1
            except Exception as e:
                print(f"[TOOL] Error visiting search results: {e}")

        if wiki_text and "No Wikipedia results found" not in wiki_text and "failed" not in wiki_text.lower():
            context_parts.append(f"\nWIKIPEDIA:\n{wiki_text}")

        # Step 4: If initial results are thin, try an alternative query
        if not context_parts or all("No " in p or "error" in p.lower() for p in context_parts):
            print("[TOOL] Initial search thin — trying alternative query")
            alt_query = focused_query.replace('"', '').replace("'", "")
            if alt_query != focused_query:
                alt_results = web_search.invoke({"query": alt_query})
                if alt_results and alt_results != "No search results found.":
                    context_parts.append(f"\nALTERNATIVE SEARCH:\n{alt_results}")

        state["context"] = "\n\n".join(context_parts) if context_parts else "No information found from web search or Wikipedia."

    else:
        # Logic / pure reasoning — no search needed
        print("[TOOL] reasoning only (no search)")
        state["context"] = ""
    return state

def synthesize_response(state: AgentState) -> AgentState:
    # If a tool produced a direct final answer (python execution), skip reasoning
    if state.get("answer") and state["label"] == "python_script":
        print(f"[SYNTHESIZE] skipped — python output: {state['answer'][:200]}")
        return state

    # For image: the vision model already answered, but wrap it through reasoning
    # to extract the precise answer from the description
    if state.get("answer") and state["label"] == "image":
        state["context"] = f"VISION MODEL OUTPUT:\n{state['answer']}"
        state["answer"] = ""  # clear so reasoning runs

    # For other_ext with context (file data), make sure reasoning runs
    if state["label"] == "other_ext" and state.get("context") and not state.get("answer"):
        pass  # context is set, reasoning will run below

    # Pass 1: chain-of-thought reasoning
    reasoning_prompt = [
        SystemMessage(content=get_prompt("reasoning_system")),
        HumanMessage(
            content=get_prompt(
                prompt_key="reasoning_user",
                question=state["question"],
                context=state["context"],
            )
        ),
    ]
    reasoning = _llm_answer.invoke(reasoning_prompt).content.strip()
    print(f"\n[REASONING]\n{reasoning}\n")

    # Try to extract FINAL ANSWER directly from reasoning text (avoids second LLM call hallucinating)
    fa_match = re.search(r"FINAL ANSWER:\s*(.+)", reasoning, re.IGNORECASE)
    if fa_match:
        answer = fa_match.group(1).strip().split('\n')[0].strip()
    elif reasoning.strip():
        # Pass 2: ask LLM to extract only if no FINAL ANSWER marker found
        extract_prompt = [
            SystemMessage(content=get_prompt("extract_system")),
            HumanMessage(
                content=get_prompt(
                    prompt_key="extract_user",
                    reasoning=reasoning,
                )
            ),
        ]
        answer = _llm_answer.invoke(extract_prompt).content.strip()
    else:
        answer = "ERROR: no reasoning produced"
    state["answer"] = answer
    return state

def format_output(state: AgentState) -> AgentState:
    txt = re.sub(r"^(final answer:?\s*)", "", state["answer"], flags=re.I).strip()

    # If question demands a single token (first name / one word), enforce it
    if any(kw in state["question"].lower() for kw in ["first name", "single word"]):
        txt = txt.split(" ")[0]

    state["answer"] = txt.rstrip(".")
    print(f"[FINAL ANSWER] {state['answer']}\n" + "-" * 60)
    return state

# --------------------------------------------------------------------------- #
#                              BUILD  THE  GRAPH                              #
# --------------------------------------------------------------------------- #
def build_graph() -> StateGraph:
    g = StateGraph(AgentState)
    g.set_entry_point("route_question")

    g.add_node("route_question", route_question)
    g.add_node("invoke_tools", call_tools)
    g.add_node("synthesize_response", synthesize_response)
    g.add_node("format_output", format_output)

    g.add_edge("route_question", "invoke_tools")
    g.add_edge("invoke_tools", "synthesize_response")
    g.add_edge("synthesize_response", "format_output")
    g.add_edge("format_output", END)

    return g.compile()

class LGAgent:
    """Callable wrapper used by run_and_submit_all."""

    def __init__(self, model_id: str | None = None, answer_model_id: str | None = None) -> None:
        global _llm_router, _llm_answer
        # Router: fast Groq model
        router_mid = model_id or GROQ_MODELS[0]["model_id"]
        _llm_router = ChatOpenAI(
            model=router_mid,
            base_url="https://api.groq.com/openai/v1",
            api_key=GROQ_API_KEY,
            timeout=60,
        )
        # Answering: higher quality OpenRouter model
        answer_mid = answer_model_id or OPENROUTER_MODELS[0]["model_id"]
        _llm_answer = ChatOpenAI(
            model=answer_mid,
            base_url="https://openrouter.ai/api/v1",
            api_key=OPENROUTER_API_KEY,
            timeout=120,
        )
        self.graph = build_graph()

    def __call__(self, question: str, task_id: str | None = None, file_name: str | None = None) -> str:
        try:
            state: AgentState = {
                "question": question,
                "label": "general",
                "context": "",
                "answer": "",
                "task_id": task_id,
                "file_name": file_name,
            }
            final = self.graph.invoke(state)

            route = final["label"]
            print(f"[ROUTE] '{route}' | Q: {question[:80]}")
            return final["answer"]
        except Exception as e:
            print("Agent error:", e)
            msg = str(e)
            # Re-raise rate-limit errors so _answer_question can fall back to the next model
            if "rate_limit_exceeded" in msg or "429" in msg or "413" in msg or "Request too large" in msg or "model_decommissioned" in msg or "decommissioned" in msg:
                raise
            return f"AGENT ERROR: {e}"

def _parse_retry_after(error_msg: str) -> float:
    """Parse the suggested wait time (seconds) from a Groq 429 error message."""
    m = re.search(r'try again in (?:(\d+)m)?(\d+(?:\.\d+)?)s', error_msg)
    if m:
        return float(m.group(1) or 0) * 60 + float(m.group(2))
    return 65.0  # safe default

def _to_str(val) -> str:
    """Ensure the submitted answer is always a plain string."""
    if isinstance(val, str):
        return val
    if isinstance(val, list):
        parts = [item.get("text", "") if isinstance(item, dict) else str(item) for item in val]
        return " ".join(parts).strip() or "ERROR: empty response"
    return str(val)


def _answer_question(item: dict) -> str:
    """Instantiate a fresh agent and answer one question, retrying on errors."""
    question_text = item["question"]
    task_id = item.get("task_id", "")
    file_name = item.get("file_name") or ""

    augmented_question = question_text

    # Try each OpenRouter answer model with Groq router
    for answer_cfg in OPENROUTER_MODELS:
        answer_model_id = answer_cfg["model_id"]
        if answer_model_id in _exhausted_models:
            print(f"[{answer_model_id}] Skipped (previously rate-limited)")
            continue
        for attempt in range(2):
            try:
                result = LGAgent(
                    model_id=GROQ_MODELS[0]["model_id"],
                    answer_model_id=answer_model_id,
                )(augmented_question, task_id=task_id, file_name=file_name)
                # Pause between questions to respect rate limits
                time.sleep(3)
                return result
            except Exception as e:
                msg = str(e)
                if "model_decommissioned" in msg or "decommissioned" in msg:
                    _exhausted_models.add(answer_model_id)
                    print(f"[{answer_model_id}] Model decommissioned — skipping permanently")
                    break
                if "rate_limit_exceeded" in msg or "429" in msg or "413" in msg or "Request too large" in msg:
                    if "on tokens per day" in msg or "TPD" in msg:
                        _exhausted_models.add(answer_model_id)
                        print(f"[{answer_model_id}] Daily token limit hit — skipping for remaining questions")
                        break
                    wait = _parse_retry_after(msg)
                    print(f"[{answer_model_id}] Rate limited — waiting {wait:.0f}s then retry")
                    time.sleep(min(wait, 30))
                    continue
                else:
                    print(f"[{answer_model_id}] Error: {msg[:200]}")
                    break  # try next model
    return "AGENT ERROR: all models exhausted"

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. Agent is instantiated per-question inside _answer_question for parallel execution
    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 (parallel)
    results_log = []
    answers_payload = []
    valid_items = [
        item for item in questions_data
        if item.get("task_id") and item.get("question") is not None
    ]
    print(f"Running agent on {len(valid_items)} questions")

    with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
        future_to_item = {
            executor.submit(_answer_question, item): item
            for item in valid_items
        }
        for future in concurrent.futures.as_completed(future_to_item):
            item = future_to_item[future]
            task_id = item["task_id"]
            question_text = item["question"]
            try:
                submitted_answer = _to_str(future.result(timeout=QUESTION_TIMEOUT))
            except concurrent.futures.TimeoutError:
                print(f"Timeout on task {task_id}")
                submitted_answer = "TIMEOUT"
            except Exception as e:
                print(f"Error running agent on task {task_id}: {e}")
                submitted_answer = f"AGENT ERROR: {e}"
            answers_payload.append({"task_id": task_id, "submitted_answer": _to_str(submitted_answer)})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})

    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)