app.py

import os
import gradio as gr
import requests
import aiohttp
import asyncio
import json
import nest_asyncio
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
from langchain_huggingface import HuggingFacePipeline
from transformers import pipeline
from langchain_core.messages import SystemMessage, HumanMessage
from tools import search_tool, multi_hop_search_tool, file_parser_tool, image_parser_tool, calculator_tool, document_retriever_tool
from tools.search import initialize_search_tools
from state import JARVISState
import pandas as pd
from dotenv import load_dotenv
import logging
from langfuse.callback import CallbackHandler

# Set up logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

# Apply nest_asyncio
nest_asyncio.apply()

# Load environment variables
load_dotenv()

# Verify environment variables
required_env_vars = ["SPACE_ID", "LANGFUSE_PUBLIC_KEY", "LANGFUSE_SECRET_KEY"]
for var in required_env_vars:
    if not os.getenv(var):
        raise ValueError(f"Environment variable {var} is not set")
logger.info(f"Environment variables loaded: SPACE_ID={os.getenv('SPACE_ID')[:10]}..., LANGFUSE_HOST={os.getenv('LANGFUSE_HOST', 'https://cloud.langfuse.com')}")

# Initialize Hugging Face model
try:
    hf_pipeline = pipeline(
        "text-generation",
        model="mistralai/Mixtral-7B-Instruct-v0.1",
        device_map="auto",
        max_new_tokens=512,
        do_sample=True,
        temperature=0.7
    )
    llm = HuggingFacePipeline(pipeline=hf_pipeline)
    logger.info("HuggingFace model initialized: mistralai/Mixtral-7B-Instruct-v0.1")
except Exception as e:
    logger.error(f"Failed to initialize HuggingFace model: {e}")
    llm = None

# Initialize search tools with LLM
try:
    initialize_search_tools(llm)
    logger.info("Search tools initialized")
except Exception as e:
    logger.error(f"Failed to initialize search tools: {e}")

# Initialize Langfuse
try:
    langfuse = CallbackHandler(
        public_key=os.getenv("LANGFUSE_PUBLIC_KEY"),
        secret_key=os.getenv("LANGFUSE_SECRET_KEY"),
        host=os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com")
    )
    logger.info("Langfuse initialized successfully")
except Exception as e:
    logger.warning(f"Failed to initialize Langfuse: {e}")
    langfuse = None

# Initialize MemorySaver
memory = MemorySaver()
use_checkpointing = True

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space/api"
GAIA_FILE_URL = "https://api.gaia-benchmark.com/files/"

# --- Helper Functions ---
def log_state(task_id: str, state: JARVISState):
    """Log intermediate state to state_log.json"""
    try:
        log_entry = {
            "task_id": task_id,
            "question": state["question"],
            "tools_needed": state["tools_needed"],
            "web_results": state["web_results"],
            "file_results": state["file_results"],
            "image_results": state["image_results"],
            "calculation_results": state["calculation_results"],
            "document_results": state["document_results"],
            "answer": state["answer"]
        }
        with open("state_log.json", "a") as f:
            json.dump(log_entry, f, indent=2)
            f.write("\n")
    except Exception as e:
        logger.error(f"Error logging state for task {task_id}: {e}")

async def test_gaia_api(task_id: str) -> bool:
    """Test connectivity to GAIA file API"""
    try:
        async with aiohttp.ClientSession() as session:
            async with session.head(f"{GAIA_FILE_URL}{task_id}", timeout=5) as resp:
                return resp.status in [200, 403, 404]
    except Exception as e:
        logger.warning(f"GAIA API test failed: {e}")
        return False

# --- Node Functions ---
async def parse_question(state: JARVISState) -> JARVISState:
    try:
        question = state["question"]
        prompt = f"""Analyze this GAIA question: {question}
        Determine which tools are needed (web_search, multi_hop_search, file_parser, image_parser, calculator, document_retriever).
        Return a JSON list of tool names."""
        if llm:
            response = await llm.ainvoke(prompt, config={"callbacks": [langfuse] if langfuse else []})
            try:
                tools_needed = json.loads(response.content)
            except json.JSONDecodeError as je:
                logger.warning(f"Invalid JSON in LLM response for task {state['task_id']}: {je}")
                tools_needed = ["web_search"]
        else:
            logger.warning("No LLM available, using default tools")
            tools_needed = ["web_search"]
        state["tools_needed"] = tools_needed
        log_state(state["task_id"], state)
        return state
    except Exception as e:
        logger.error(f"Error parsing question for task {state['task_id']}: {e}")
        state["tools_needed"] = []
        log_state(state["task_id"], state)
        return state

async def tool_dispatcher(state: JARVISState) -> JARVISState:
    try:
        tools_needed = state["tools_needed"]
        updated_state = state.copy()
        can_download_files = await test_gaia_api(updated_state["task_id"])

        for tool in tools_needed:
            try:
                if tool == "web_search" or tool == "multi_hop_search":
                    result = await web_search_agent(updated_state)
                    updated_state["web_results"].extend(result["web_results"])
                elif tool == "file_parser" and can_download_files:
                    result = await file_parser_agent(updated_state)
                    updated_state["file_results"] = result["file_results"]
                elif tool == "image_parser" and can_download_files:
                    result = await image_parser_agent(updated_state)
                    updated_state["image_results"] = result["image_results"]
                elif tool == "calculator":
                    result = await calculator_agent(updated_state)
                    updated_state["calculation_results"] = result["calculation_results"]
                elif tool == "document_retriever" and can_download_files:
                    result = await document_retriever_agent(updated_state)
                    updated_state["document_results"] = result["document_results"]
            except Exception as e:
                logger.warning(f"Error in tool {tool} for task {updated_state['task_id']}: {e}")
        
        log_state(updated_state["task_id"], updated_state)
        return updated_state
    except Exception as e:
        logger.error(f"Error in tool dispatcher for task {state['task_id']}: {e}")
        log_state(state["task_id"], state)
        return state

async def web_search_agent(state: JARVISState) -> JARVISState:
    try:
        results = []
        if "web_search" in state["tools_needed"]:
            result = await search_tool.invoke({"query": state["question"]})
            results.append(result)
        if "multi_hop_search" in state["tools_needed"]:
            result = await multi_hop_search_tool.invoke({"query": state["question"], "steps": 3})
            results.append(result)
        return {"web_results": results}
    except Exception as e:
        logger.error(f"Error in web search for task {state['task_id']}: {e}")
        return {"web_results": []}

async def file_parser_agent(state: JARVISState) -> JARVISState:
    try:
        if "file_parser" in state["tools_needed"]:
            file_type = "csv" if "data" in state["question"].lower() else "txt"
            result = await file_parser_tool.aparse(state["task_id"], file_type=file_type)
            return {"file_results": result}
        return {"file_results": ""}
    except Exception as e:
        logger.error(f"Error in file parser for task {state['task_id']}: {e}")
        return {"file_results": "File parsing failed"}

async def image_parser_agent(state: JARVISState) -> JARVISState:
    try:
        if "image_parser" in state["tools_needed"]:
            task = "match" if "fruits" in state["question"].lower() else "describe"
            match_query = "fruits" if task == "match" else ""
            file_path = f"temp_{state['task_id']}.jpg"
            if not os.path.exists(file_path):
                logger.warning(f"Image file not found for task {state['task_id']}")
                return {"image_results": "Image file not found"}
            result = await image_parser_tool.aparse(
                file_path, task=task, match_query=match_query
            )
            return {"image_results": result}
        return {"image_results": ""}
    except Exception as e:
        logger.error(f"Error in image parser for task {state['task_id']}: {e}")
        return {"image_results": "Image parsing failed"}

async def calculator_agent(state: JARVISState) -> JARVISState:
    try:
        if "calculator" in state["tools_needed"]:
            prompt = f"Extract a mathematical expression from: {state['question']}\n{state['file_results']}"
            if llm:
                response = await llm.ainvoke(prompt, config={"callbacks": [langfuse] if langfuse else []})
                expression = response.content
            else:
                expression = "0"
            result = await calculator_tool.aparse(expression)
            return {"calculation_results": result}
        return {"calculation_results": ""}
    except Exception as e:
        logger.error(f"Error in calculator for task {state['task_id']}: {e}")
        return {"calculation_results": "Calculation failed"}

async def document_retriever_agent(state: JARVISState) -> JARVISState:
    try:
        if "document_retriever" in state["tools_needed"]:
            file_type = "txt" if "menu" in state["question"].lower() else "csv"
            if "report" in state["question"].lower() or "document" in state["question"].lower():
                file_type = "pdf"
            result = await document_retriever_tool.aparse(
                state["task_id"], state["question"], file_type=file_type
            )
            return {"document_results": result}
        return {"document_results": ""}
    except Exception as e:
        logger.error(f"Error in document retriever for task {state['task_id']}: {e}")
        return {"document_results": "Document retrieval failed"}

async def reasoning_agent(state: JARVISState) -> JARVISState:
    try:
        prompt = f"""Question: {state['question']}
        Web Results: {state['web_results']}
        File Results: {state['file_results']}
        Image Results: {state['image_results']}
        Calculation Results: {state['calculation_results']}
        Document Results: {state['document_results']}
        Synthesize an exact-match answer for the GAIA benchmark.
        Output only the answer (e.g., '90', 'White;5876')."""
        if llm:
            response = await llm.ainvoke(
                [
                    SystemMessage(content="You are JARVIS, a precise assistant for the GAIA benchmark. Provide exact answers only."),
                    HumanMessage(content=prompt)
                ],
                config={"callbacks": [langfuse] if langfuse else []}
            )
            answer = response.content.strip()
        else:
            answer = "Unknown"
        state["answer"] = answer
        log_state(state["task_id"], state)
        return state
    except Exception as e:
        logger.error(f"Error in reasoning for task {state['task_id']}: {e}")
        state["answer"] = "Error in reasoning"
        log_state(state["task_id"], state)
        return state

def router(state: JARVISState) -> str:
    if state["tools_needed"]:
        return "tool_dispatcher"
    return "reasoning"

# --- Define StateGraph ---
workflow = StateGraph(JARVISState)
workflow.add_node("parse", parse_question)
workflow.add_node("tool_dispatcher", tool_dispatcher)
workflow.add_node("reasoning", reasoning_agent)

workflow.set_entry_point("parse")
workflow.add_conditional_edges(
    "parse",
    router,
    {
        "tool_dispatcher": "tool_dispatcher",
        "reasoning": "reasoning"
    }
)
workflow.add_edge("tool_dispatcher", "reasoning")
workflow.add_edge("reasoning", END)

# Compile graph
graph = workflow.compile(checkpointer=memory if use_checkpointing else None)

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self):
        logger.info("BasicAgent initialized.")

    async def process_question(self, task_id: str, question: str) -> str:
        file_type = "jpg" if "image" in question.lower() else "txt"
        if "menu" in question.lower() or "report" in question.lower() or "document" in question.lower():
            file_type = "pdf"
        elif "data" in question.lower():
            file_type = "csv"

        file_path = f"temp_{task_id}.{file_type}"
        if await test_gaia_api(task_id):
            try:
                async with aiohttp.ClientSession() as session:
                    async with session.get(f"{GAIA_FILE_URL}{task_id}") as resp:
                        if resp.status == 200:
                            with open(file_path, "wb") as f:
                                f.write(await resp.read())
                        else:
                            logger.warning(f"Failed to download file for task {task_id}: HTTP {resp.status}")
            except Exception as e:
                logger.error(f"Error downloading file for task {task_id}: {e}")

        state = JARVISState(
            task_id=task_id,
            question=question,
            tools_needed=[],
            web_results=[],
            file_results="",
            image_results="",
            calculation_results="",
            document_results="",
            messages=[],
            answer=""
        )
        try:
            config = {"configurable": {"thread_id": task_id}} if use_checkpointing else {}
            result = await graph.ainvoke(state, config=config)
            return result["answer"] or "No answer generated"
        except Exception as e:
            logger.error(f"Error processing task {task_id}: {e}")
            return f"Error: {str(e)}"
        finally:
            if os.path.exists(file_path):
                try:
                    os.remove(file_path)
                except Exception as e:
                    logger.error(f"Error removing file {file_path}: {e}")

    async def async_call(self, question: str, task_id: str) -> str:
        return await self.process_question(task_id, question)

    def __call__(self, question: str, task_id: str = None) -> str:
        logger.info(f"Agent received question (first 50 chars): {question[:50]}...")
        if task_id is None:
            logger.warning("task_id not provided, using placeholder")
            task_id = "placeholder_task_id"
        try:
            try:
                loop = asyncio.get_event_loop()
            except RuntimeError:
                loop = asyncio.new_event_loop()
                asyncio.set_event_loop(loop)
            return loop.run_until_complete(self.async_call(question, task_id))
        finally:
            pass

# --- Main Function ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")
    if not profile:
        logger.error("User not logged in.")
        return "Please Login to Hugging Face with the button.", None
    username = f"{profile.username}"
    logger.info(f"User logged in: {username}")

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    try:
        agent = BasicAgent()
    except Exception as e:
        logger.error(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    logger.info(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:
            logger.error("Fetched questions list is empty.")
            return "Fetched questions list is empty or invalid format.", None
        logger.info(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        logger.error(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None

    results_log = []
    answers_payload = []
    logger.info(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:
            logger.warning(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            submitted_answer = agent(question_text, task_id)
            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:
            logger.error(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:
        logger.error("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}
    logger.info(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=120)
        response.raise_for_status()
        result_data = response.json()
        logger.info(f"Server response: {result_data}")
        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.')}"
        )
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except Exception as e:
        logger.error(f"Submission failed: {e}")
        results_df = pd.DataFrame(results_log)
        return f"Submission Failed: {e}", results_df

# --- Build Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# JARVIS Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1. Log in to your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run the JARVIS agent, and submit answers.

        ---
        **Disclaimers:**
        The agent uses a local Hugging Face model (Mixtral-7B) and async tools for the GAIA benchmark.
        """
    )

    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__":
    logger.info("\n" + "-"*30 + " App Starting " + "-"*30)
    space_id = os.getenv("SPACE_ID")
    logger.info(f"SPACE_ID: {space_id}")
    logger.info("Launching Gradio Interface...")
    demo.launch(debug=True, share=False)