app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from typing import Any
import re

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Advanced Modular Agent Implementation ---
import json
import logging
import mimetypes
import openpyxl
import numpy as np
from datetime import datetime
from io import BytesIO
from PIL import Image
import subprocess
import tempfile
from huggingface_hub import InferenceClient
import cv2
import torch
from bs4 import BeautifulSoup
import openai
import magic  # for robust file type detection

logging.basicConfig(filename='gaia_agent.log', level=logging.INFO, format='%(asctime)s %(levelname)s:%(message)s')
logger = logging.getLogger(__name__)
HF_TOKEN = os.environ.get("HF_TOKEN", "")

def llama3_chat(prompt):
    try:
        client = InferenceClient(provider="fireworks-ai", api_key=HF_TOKEN)
        completion = client.chat.completions.create(
            model="meta-llama/Llama-3.1-8B-Instruct",
            messages=[{"role": "user", "content": prompt}],
        )
        return completion.choices[0].message.content
    except Exception as e:
        logging.error(f"llama3_chat error: {e}")
        return f"LLM error: {e}"

def mixtral_chat(prompt):
    try:
        client = InferenceClient(provider="hf-inference", api_key=HF_TOKEN)
        completion = client.chat.completions.create(
            model="mistralai/Mixtral-8x7B-Instruct-v0.1",
            messages=[{"role": "user", "content": prompt}],
        )
        return completion.choices[0].message.content
    except Exception as e:
        logging.error(f"mixtral_chat error: {e}")
        return f"LLM error: {e}"

def extractive_qa(question, context):
    try:
        client = InferenceClient(provider="hf-inference", api_key=HF_TOKEN)
        answer = client.question_answering(
            question=question,
            context=context,
            model="deepset/roberta-base-squad2",
        )
        return answer["answer"]
    except Exception as e:
        logging.error(f"extractive_qa error: {e}")
        return f"QA error: {e}"

def table_qa(query, table):
    try:
        client = InferenceClient(provider="hf-inference", api_key=HF_TOKEN)
        answer = client.table_question_answering(
            query=query,
            table=table,
            model="google/tapas-large-finetuned-wtq",
        )
        return answer["answer"]
    except Exception as e:
        logging.error(f"table_qa error: {e}")
        return f"Table QA error: {e}"

def asr_transcribe(audio_path):
    try:
        import torchaudio
        from transformers import pipeline
        asr = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
        result = asr(audio_path)
        return result["text"]
    except Exception as e:
        logging.error(f"asr_transcribe error: {e}")
        return f"ASR error: {e}"

def image_caption(image_path):
    try:
        from transformers import BlipProcessor, BlipForConditionalGeneration
        from PIL import Image
        processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
        model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
        raw_image = Image.open(image_path).convert('RGB')
        inputs = processor(raw_image, return_tensors="pt")
        out = model.generate(**inputs)
        return processor.decode(out[0], skip_special_tokens=True)
    except Exception as e:
        logging.error(f"image_caption error: {e}")
        return f"Image captioning error: {e}"

def code_analysis(py_path):
    try:
        with open(py_path) as f:
            code = f.read()
        with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as tmp:
            tmp.write(code)
            tmp_path = tmp.name
        try:
            result = subprocess.run([
                "python3", tmp_path
            ], capture_output=True, text=True, timeout=5)
            if result.returncode == 0:
                output = result.stdout.strip().split('\n')
                return output[-1] if output else ''
            else:
                logging.error(f"code_analysis subprocess error: {result.stderr}")
                return f"Code error: {result.stderr}"
        except subprocess.TimeoutExpired:
            logging.error("code_analysis timeout")
            return "Code execution timed out"
        finally:
            os.remove(tmp_path)
    except Exception as e:
        logging.error(f"code_analysis error: {e}")
        return f"Code analysis error: {e}"

def youtube_video_qa(youtube_url, question):
    import subprocess
    import tempfile
    import os
    from transformers import pipeline
    try:
        with tempfile.TemporaryDirectory() as tmpdir:
            # Download video
            video_path = os.path.join(tmpdir, "video.mp4")
            cmd = ["yt-dlp", "-f", "mp4", "-o", video_path, youtube_url]
            subprocess.run(cmd, check=True)
            # Extract audio for ASR
            audio_path = os.path.join(tmpdir, "audio.mp3")
            cmd_audio = ["yt-dlp", "-f", "bestaudio", "--extract-audio", "--audio-format", "mp3", "-o", audio_path, youtube_url]
            subprocess.run(cmd_audio, check=True)
            # Transcribe audio
            asr = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
            result = asr(audio_path)
            transcript = result["text"]
            # Extract frames for vision QA
            cap = cv2.VideoCapture(video_path)
            frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
            fps = int(cap.get(cv2.CAP_PROP_FPS))
            frames = []
            for i in range(0, frame_count, max(1, fps*5)):
                cap.set(cv2.CAP_PROP_POS_FRAMES, i)
                ret, frame = cap.read()
                if not ret:
                    break
                img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
                frames.append(img)
            cap.release()
            # Object detection (YOLOv8)
            try:
                from ultralytics import YOLO
                yolo = YOLO("yolov8n.pt")
                detections = []
                for img in frames:
                    results = yolo(np.array(img))
                    for r in results:
                        for c in r.boxes.cls:
                            detections.append(yolo.model.names[int(c)])
                detection_summary = {}
                for obj in detections:
                    detection_summary[obj] = detection_summary.get(obj, 0) + 1
            except Exception as e:
                logging.error(f"YOLOv8 error: {e}")
                detection_summary = {}
            # Image captioning (BLIP)
            try:
                from transformers import BlipProcessor, BlipForConditionalGeneration
                processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
                model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
                captions = []
                for img in frames:
                    inputs = processor(img, return_tensors="pt")
                    out = model.generate(**inputs)
                    captions.append(processor.decode(out[0], skip_special_tokens=True))
            except Exception as e:
                logging.error(f"BLIP error: {e}")
                captions = []
            context = f"Transcript: {transcript}\nCaptions: {' | '.join(captions)}\nDetections: {detection_summary}"
            answer = extractive_qa(question, context)
            return answer
    except Exception as e:
        logging.error(f"YouTube video QA error: {e}")
        return f"Video analysis error: {e}"

def web_search_duckduckgo(query, max_results=5):
    """DuckDuckGo web search tool: returns top snippets and URLs."""
    try:
        import duckduckgo_search
        results = duckduckgo_search.DuckDuckGoSearch().search(query, max_results=max_results)
        snippets = []
        for r in results:
            snippet = f"Title: {r['title']}\nSnippet: {r['body']}\nURL: {r['href']}"
            snippets.append(snippet)
        return '\n---\n'.join(snippets)
    except Exception as e:
        logging.error(f"web_search_duckduckgo error: {e}")
        return f"Web search error: {e}"

def gpt4_chat(prompt, api_key=None):
    """OpenAI GPT-4.1 chat completion."""
    try:
        api_key = api_key or os.environ.get("OPENAI_API_KEY", "")
        if not api_key:
            return "No OpenAI API key provided."
        response = openai.ChatCompletion.create(
            model="gpt-4-1106-preview",
            messages=[{"role": "system", "content": "You are a general AI assistant. Answer using as few words as possible, in the required format. Use tools as needed, and only output the answer."},
                     {"role": "user", "content": prompt}],
            api_key=api_key,
        )
        return response.choices[0].message['content'].strip()
    except Exception as e:
        logging.error(f"gpt4_chat error: {e}")
        return f"GPT-4 error: {e}"

TOOL_REGISTRY = {
    "llama3_chat": llama3_chat,
    "mixtral_chat": mixtral_chat,
    "extractive_qa": extractive_qa,
    "table_qa": table_qa,
    "asr_transcribe": asr_transcribe,
    "image_caption": image_caption,
    "code_analysis": code_analysis,
    "youtube_video_qa": youtube_video_qa,
    "web_search_duckduckgo": web_search_duckduckgo,
    "gpt4_chat": gpt4_chat,
}

# --- Utility: Robust file type detection ---
def detect_file_type_magic(file_name):
    try:
        mime = magic.Magic(mime=True)
        filetype = mime.from_file(file_name)
        if 'audio' in filetype:
            return 'audio'
        elif 'image' in filetype:
            return 'image'
        elif 'python' in filetype or file_name.endswith('.py'):
            return 'code'
        elif 'spreadsheet' in filetype or file_name.endswith('.xlsx'):
            return 'excel'
        elif 'csv' in filetype or file_name.endswith('.csv'):
            return 'csv'
        elif 'json' in filetype or file_name.endswith('.json'):
            return 'json'
        elif 'text' in filetype or file_name.endswith(('.txt', '.md')):
            return 'text'
        else:
            return 'unknown'
    except Exception as e:
        logger.error(f"magic file type detection error: {e}")
        return 'unknown'

# --- Improved prompt template for LLMs ---
def build_prompt(context, question):
    return f"""
Context:
{context}

Question:
{question}

Answer:
"""

# --- Centralized Output Formatting & Normalization ---
def gaia_normalize_answer(answer):
    """Normalize answer for GAIA: remove units, articles, extra text, and ensure concise, factual output."""
    if not isinstance(answer, str):
        answer = str(answer)
    # Remove common articles and units unless required
    answer = answer.strip()
    answer = re.sub(r"\b(the|a|an)\b", "", answer, flags=re.IGNORECASE)
    answer = re.sub(r"\s+", " ", answer)
    # Remove currency, percent, or units unless specified (GAIA rules)
    answer = re.sub(r"\$|%|USD|dollars|euros|eur|\bpercent\b", "", answer, flags=re.IGNORECASE)
    # Remove leading/trailing punctuation
    answer = answer.strip(' .,:;\n\t')
    return answer

# --- Reasoning Planner for Tool Chaining ---
def reasoning_planner(question, file_type, tools):
    """Plan the sequence of tools to use for a question. Uses LLM or heuristic."""
    # Heuristic: if file_type is known, use the corresponding tool; else, use web search + LLM
    if file_type == 'audio':
        return ['asr_transcribe', 'llama3_chat']
    elif file_type == 'image':
        return ['image_caption', 'llama3_chat']
    elif file_type == 'code':
        return ['code_analysis', 'llama3_chat']
    elif file_type in ['excel', 'csv']:
        return ['table_qa']
    elif 'youtube.com' in question or 'youtu.be' in question:
        return ['youtube_video_qa']
    elif any(w in question.lower() for w in ['wikipedia', 'who', 'when', 'where', 'what', 'how', 'find', 'search']):
        return ['web_search_duckduckgo', 'llama3_chat']
    else:
        return ['llama3_chat']

# --- Improved RAG: Context Retrieval & Chunking ---
def retrieve_context(question, context_files, max_chunks=3):
    """Retrieve relevant context chunks from large files for RAG."""
    # Simple keyword search for now; can be replaced with semantic search
    relevant_chunks = []
    for file_path in context_files:
        try:
            with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
                text = f.read()
            # Split into chunks (e.g., 500 words)
            chunks = [text[i:i+2000] for i in range(0, len(text), 2000)]
            for chunk in chunks:
                if any(word.lower() in chunk.lower() for word in question.split()):
                    relevant_chunks.append(chunk)
                    if len(relevant_chunks) >= max_chunks:
                        break
        except Exception as e:
            logger.error(f"retrieve_context error: {e}")
    return '\n'.join(relevant_chunks)

# --- Modular Tool Registry & Chaining ---
class ToolRegistry:
    """Central registry for tools. Allows easy addition and chaining."""
    def __init__(self, tools):
        self.tools = tools
    def get(self, name):
        return self.tools.get(name)
    def add(self, name, func):
        self.tools[name] = func
    def list(self):
        return list(self.tools.keys())

# --- Refactored ModularGAIAAgent ---
class ModularGAIAAgent:
    """GAIA-compliant agent with robust reasoning, tool chaining, RAG, and output normalization."""
    def __init__(self, api_url=DEFAULT_API_URL, tool_registry=None, context_files=None):
        self.api_url = api_url
        self.tools = ToolRegistry(tool_registry or TOOL_REGISTRY)
        self.reasoning_trace = []
        self.file_cache = set(os.listdir('.'))
        self.context_files = context_files or []

    def fetch_questions(self, from_api=True, questions_path="Hugging Face Questions"):
        """Fetch questions from API or local file."""
        try:
            if from_api:
                r = requests.get(f"{self.api_url}/questions")
                r.raise_for_status()
                return r.json()
            else:
                with open(questions_path) as f:
                    data = f.read()
                start = data.find("[")
                end = data.rfind("]") + 1
                questions = json.loads(data[start:end])
                return questions
        except Exception as e:
            logger.error(f"fetch_questions error: {e}")
            return []

    def download_file(self, file_id, file_name=None):
        """Download file if not present locally."""
        try:
            if not file_name:
                file_name = file_id
            if file_name in self.file_cache:
                return file_name
            url = f"{self.api_url}/files/{file_id}"
            r = requests.get(url)
            if r.status_code == 200:
                with open(file_name, "wb") as f:
                    f.write(r.content)
                self.file_cache.add(file_name)
                return file_name
            else:
                self.reasoning_trace.append(f"Failed to download file {file_id} (status {r.status_code})")
                logger.error(f"Failed to download file {file_id} (status {r.status_code})")
                return None
        except Exception as e:
            logger.error(f"download_file error: {e}")
            self.reasoning_trace.append(f"Download error: {e}")
            return None

    def detect_file_type(self, file_name):
        """Detect file type using magic and extension as fallback."""
        file_type = detect_file_type_magic(file_name)
        if file_type == 'unknown':
            ext = os.path.splitext(file_name)[-1].lower()
            if ext in ['.mp3', '.wav', '.flac']:
                return 'audio'
            elif ext in ['.png', '.jpg', '.jpeg', '.bmp']:
                return 'image'
            elif ext in ['.py']:
                return 'code'
            elif ext in ['.xlsx']:
                return 'excel'
            elif ext in ['.csv']:
                return 'csv'
            elif ext in ['.json']:
                return 'json'
            elif ext in ['.txt', '.md']:
                return 'text'
            else:
                return 'unknown'
        return file_type

    def analyze_file(self, file_name, file_type):
        """Analyze file and return context for the question."""
        try:
            if file_type == 'audio':
                transcript = self.tools.get('asr_transcribe')(file_name)
                self.reasoning_trace.append(f"Transcribed audio: {transcript[:100]}...")
                return transcript
            elif file_type == 'image':
                caption = self.tools.get('image_caption')(file_name)
                self.reasoning_trace.append(f"Image caption: {caption}")
                return caption
            elif file_type == 'code':
                result = self.tools.get('code_analysis')(file_name)
                self.reasoning_trace.append(f"Code analysis result: {result}")
                return result
            elif file_type == 'excel':
                wb = openpyxl.load_workbook(file_name)
                ws = wb.active
                data = list(ws.values)
                headers = data[0]
                table = [dict(zip(headers, row)) for row in data[1:]]
                self.reasoning_trace.append(f"Excel table loaded: {table[:2]}...")
                return table
            elif file_type == 'csv':
                df = pd.read_csv(file_name)
                table = df.to_dict(orient='records')
                self.reasoning_trace.append(f"CSV table loaded: {table[:2]}...")
                return table
            elif file_type == 'json':
                with open(file_name) as f:
                    data = json.load(f)
                self.reasoning_trace.append(f"JSON loaded: {str(data)[:100]}...")
                return data
            elif file_type == 'text':
                with open(file_name) as f:
                    text = f.read()
                self.reasoning_trace.append(f"Text loaded: {text[:100]}...")
                return text
            else:
                self.reasoning_trace.append(f"Unknown file type: {file_name}")
                logger.warning(f"Unknown file type: {file_name}")
                return None
        except Exception as e:
            logger.error(f"analyze_file error: {e}")
            self.reasoning_trace.append(f"Analyze file error: {e}")
            return None

    def answer_question(self, question_obj):
        self.reasoning_trace = []
        q = question_obj["question"]
        file_name = question_obj.get("file_name", "")
        file_content = None
        file_type = None
        if file_name:
            file_id = file_name.split('.')[0]
            local_file = self.download_file(file_id, file_name)
            if local_file:
                file_type = self.detect_file_type(local_file)
                file_content = self.analyze_file(local_file, file_type)
        # RAG: retrieve context if needed
        rag_context = ''
        if not file_content and self.context_files:
            rag_context = retrieve_context(q, self.context_files)
            if rag_context:
                self.reasoning_trace.append(f"RAG context used: {rag_context[:200]}...")
        # Reasoning planner: decide tool chain
        tool_names = reasoning_planner(q, file_type, self.tools.list())
        answer = None
        context = file_content or rag_context
        for tool_name in tool_names:
            tool = self.tools.get(tool_name)
            try:
                logger.info(f"Using tool: {tool_name} | Question: {q} | Context: {str(context)[:200]}")
                if tool_name == 'web_search_duckduckgo':
                    context = tool(q)
                    answer = llama3_chat(build_prompt(context, q))
                elif tool_name == 'table_qa' and file_content:
                    answer = tool(q, file_content)
                elif tool_name in ['asr_transcribe', 'image_caption', 'code_analysis'] and file_content:
                    answer = tool(file_name)
                elif tool_name == 'youtube_video_qa':
                    answer = tool(q, q)
                else:
                    if context:
                        answer = llama3_chat(build_prompt(context, q))
                    else:
                        answer = tool(q)
                if answer:
                    break
            except Exception as e:
                logger.error(f"Tool {tool_name} error: {e}")
                self.reasoning_trace.append(f"Tool {tool_name} error: {e}")
                continue
        self.reasoning_trace.append(f"Tools used: {tool_names}")
        self.reasoning_trace.append(f"Final answer: {answer}")
        return gaia_normalize_answer(answer), self.reasoning_trace

# --- Basic Agent Definition (now wraps ModularGAIAAgent) ---
class BasicAgent:
    def __init__(self):
        print("BasicAgent (GAIA Modular Agent) initialized.")
        self.agent = ModularGAIAAgent()
    def __call__(self, question: str, file_name: str = "") -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        try:
            answer, trace = self.agent.answer_question({"task_id": "manual", "question": question, "file_name": file_name})
            print(f"Agent returning answer: {answer}")
            return answer
        except Exception as e:
            print(f"Agent error: {e}")
            return f"AGENT ERROR: {e}"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    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"
    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)
    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
    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")
        file_name = item.get("file_name", "")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            submitted_answer = agent(question_text, file_name)
            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)
    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)
    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)