app.py

import os
import re
import gradio as gr
import requests
import pandas as pd
from huggingface_hub import InferenceClient
from duckduckgo_search import DDGS
import wikipediaapi
from bs4 import BeautifulSoup
import pdfplumber
import pytube

# === CONFIG ===
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
HF_TOKEN = os.environ.get("HF_TOKEN")

ADVANCED_MODELS = [
    "deepseek-ai/DeepSeek-R1",
    "deepseek-ai/DeepSeek-V2-Chat",
    "Qwen/Qwen2-72B-Instruct",
    "mistralai/Mixtral-8x22B-Instruct-v0.1",
    "meta-llama/Meta-Llama-3-70B-Instruct"
]

wiki_api = wikipediaapi.Wikipedia(language="en", user_agent="SmartAgent/1.0 (chockqoteewy@gmail.com)")

# === UTILS ===
def extract_links(text):
    if not text:
        return []
    url_pattern = re.compile(r'(https?://[^\s\)\],]+)')
    return url_pattern.findall(text)

def download_file(url, out_dir="tmp_files"):
    os.makedirs(out_dir, exist_ok=True)
    filename = url.split("/")[-1].split("?")[0]
    local_path = os.path.join(out_dir, filename)
    try:
        r = requests.get(url, timeout=30)
        r.raise_for_status()
        with open(local_path, "wb") as f:
            f.write(r.content)
        return local_path
    except Exception:
        return None

def summarize_excel(file_path):
    try:
        df = pd.read_excel(file_path)
        # Heuristic: Sum column with "total" or "sales" in name, excluding drinks
        df.columns = [col.lower() for col in df.columns]
        item_col = next((col for col in df.columns if "item" in col or "menu" in col), None)
        total_col = next((col for col in df.columns if "total" in col or "sales" in col or "amount" in col), None)
        if not item_col or not total_col:
            return f"Excel columns: {', '.join(df.columns)}. Could not find item/total columns."
        df_food = df[~df[item_col].str.lower().str.contains("drink|beverage|soda|juice", na=False)]
        total = df_food[total_col].astype(float).sum()
        return f"{total:.2f}"
    except Exception as e:
        return f"Excel error: {e}"

def summarize_csv(file_path):
    try:
        df = pd.read_csv(file_path)
        # Same logic as summarize_excel
        df.columns = [col.lower() for col in df.columns]
        item_col = next((col for col in df.columns if "item" in col or "menu" in col), None)
        total_col = next((col for col in df.columns if "total" in col or "sales" in col or "amount" in col), None)
        if not item_col or not total_col:
            return f"CSV columns: {', '.join(df.columns)}. Could not find item/total columns."
        df_food = df[~df[item_col].str.lower().str.contains("drink|beverage|soda|juice", na=False)]
        total = df_food[total_col].astype(float).sum()
        return f"{total:.2f}"
    except Exception as e:
        return f"CSV error: {e}"

def summarize_pdf(file_path):
    try:
        with pdfplumber.open(file_path) as pdf:
            first_page = pdf.pages[0].extract_text()
            return f"PDF text sample: {first_page[:1000]}"
    except Exception as e:
        return f"PDF error: {e}"

def summarize_txt(file_path):
    try:
        with open(file_path, encoding='utf-8') as f:
            txt = f.read()
        return f"TXT file sample: {txt[:1000]}"
    except Exception as e:
        return f"TXT error: {e}"

def analyze_file(file_path):
    file_path = file_path.lower()
    if file_path.endswith((".xlsx", ".xls")):
        return summarize_excel(file_path)
    elif file_path.endswith(".csv"):
        return summarize_csv(file_path)
    elif file_path.endswith(".pdf"):
        return summarize_pdf(file_path)
    elif file_path.endswith(".txt"):
        return summarize_txt(file_path)
    else:
        return f"Unsupported file type: {file_path}"

def analyze_webpage(url):
    try:
        r = requests.get(url, timeout=20)
        soup = BeautifulSoup(r.text, "lxml")
        title = soup.title.string if soup.title else "No title"
        paragraphs = [p.get_text() for p in soup.find_all("p")]
        article_sample = "\n".join(paragraphs[:5])
        return f"Webpage Title: {title}\nContent sample:\n{article_sample[:1000]}"
    except Exception as e:
        return f"Webpage error: {e}"

def analyze_youtube(url):
    try:
        yt = pytube.YouTube(url)
        captions = yt.captions.get_by_language_code('en')
        if captions:
            text = captions.generate_srt_captions()
            return f"YouTube Transcript sample: {text[:800]}"
        else:
            return f"No English captions found for {url}"
    except Exception as e:
        return f"YouTube error: {e}"

def duckduckgo_search(query):
    try:
        with DDGS() as ddgs:
            results = [r for r in ddgs.text(query, max_results=3)]
            bodies = [r.get("body", "") for r in results if r.get("body")]
            return "\n".join(bodies) if bodies else None
    except Exception:
        return None

def wikipedia_search(query):
    try:
        page = wiki_api.page(query)
        if page.exists() and page.summary:
            return page.summary
    except Exception:
        return None
    return None

def llm_conversational(query):
    for model_id in ADVANCED_MODELS:
        try:
            hf_client = InferenceClient(model_id, token=HF_TOKEN)
            result = hf_client.conversational(
                messages=[{"role": "user", "content": query}],
                max_new_tokens=384,
            )
            if isinstance(result, dict) and "generated_text" in result:
                return result["generated_text"]
            elif hasattr(result, "generated_text"):
                return result.generated_text
            elif isinstance(result, str):
                return result
        except Exception:
            continue
    return "LLM error: No advanced conversational models succeeded."

# === TASK-SPECIFIC HANDLERS (expandable) ===
def handle_grocery_vegetables(question):
    """Extract vegetables from a list in the question."""
    match = re.search(r"list I have so far: (.*)", question)
    if not match:
        return "Could not parse item list."
    items = [i.strip().lower() for i in match.group(1).split(",")]
    vegetables = [
        "broccoli", "celery", "lettuce", "zucchini", "green beans", "sweet potatoes", "bell pepper"
    ]
    result = sorted([item for item in items if item in vegetables])
    return ", ".join(result)

# === MAIN AGENT ===
class SmartAgent:
    def __call__(self, question: str) -> str:
        # Task: Grocery vegetables
        if "vegetables" in question.lower() and "categorize" in question.lower():
            return handle_grocery_vegetables(question)
        # Download and analyze any file links
        links = extract_links(question)
        for url in links:
            if url.endswith((".xlsx", ".xls", ".csv", ".pdf", ".txt")):
                local = download_file(url)
                if local:
                    return analyze_file(local)
            elif "youtube.com" in url or "youtu.be" in url:
                return analyze_youtube(url)
            else:
                return analyze_webpage(url)
        # Wikipedia
        wiki_result = wikipedia_search(question)
        if wiki_result:
            return wiki_result
        # DuckDuckGo
        ddg_result = duckduckgo_search(question)
        if ddg_result:
            return ddg_result
        # Top LLMs
        return llm_conversational(question)

# === SUBMISSION LOGIC ===
def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")
    if profile:
        username = profile.username
    else:
        return "Please Login to Hugging Face with the button.", None

    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url = f"{DEFAULT_API_URL}/submit"

    agent = SmartAgent()
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    try:
        response = requests.get(questions_url, timeout=20)
        response.raise_for_status()
        questions_data = response.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    results_log = []
    answers_payload = []

    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or not question_text:
            continue
        submitted_answer = agent(question_text)
        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})

    if not answers_payload:
        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}

    try:
        response = requests.post(submit_url, json=submission_data, timeout=90)
        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.')}"
        )
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)

# === GRADIO UI ===
with gr.Blocks() as demo:
    gr.Markdown("# Smart Agent Evaluation Runner")
    gr.Markdown("""
        **Instructions:**
        1. Clone this space, define your agent logic, tools, packages, etc.
        2. Log in to Hugging Face.
        3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
    """)
    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__":
    demo.launch(debug=True, share=False)