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

# ==== CONFIG ====
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
HF_TOKEN = os.getenv("HF_TOKEN")
GROK_API_KEY = os.getenv("GROK_API_KEY") or "xai-AyJXz3OAAMuQiOrPzPptUWTmsEyI9vywPpbV19S1nCpXXKWoKLqOoGc61RazPPui2fx4Ekb1durXccqz"

CONVERSATIONAL_MODELS = [
    "deepseek-ai/DeepSeek-LLM",
    "HuggingFaceH4/zephyr-7b-beta",
    "mistralai/Mistral-7B-Instruct-v0.2"
]

wiki_api = wikipediaapi.Wikipedia(language="en", user_agent="SmartAgent/1.0 ([email protected])")

# ==== UTILITY: Link/file detection ====
def extract_links(text):
    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=20)
        r.raise_for_status()
        with open(local_path, "wb") as f:
            f.write(r.content)
        return local_path
    except Exception:
        return None

# ==== File/Link Analyzers ====
def analyze_file(file_path):
    if file_path.endswith((".xlsx", ".xls")):
        try:
            df = pd.read_excel(file_path)
            return f"Excel summary: {df.head().to_markdown(index=False)}"
        except Exception as e:
            return f"Excel error: {e}"
    elif file_path.endswith(".csv"):
        try:
            df = pd.read_csv(file_path)
            return f"CSV summary: {df.head().to_markdown(index=False)}"
        except Exception as e:
            return f"CSV error: {e}"
    elif file_path.endswith(".pdf"):
        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}"
    elif file_path.endswith(".txt"):
        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}"
    else:
        return f"Unsupported file type: {file_path}"
    
def analyze_webpage(url):
    try:
        r = requests.get(url, timeout=15)
        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[:1200]}"
    except Exception as e:
        return f"Webpage error: {e}"

# ==== SEARCH TOOLS ====
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):
    last_error = None
    for model_id in CONVERSATIONAL_MODELS:
        try:
            hf_client = InferenceClient(model_id, token=HF_TOKEN)
            # Try conversational if available, else fallback to text_generation
            if hasattr(hf_client, "conversational"):
                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
                else:
                    continue
            result = hf_client.text_generation(query, max_new_tokens=384)
            if isinstance(result, dict) and "generated_text" in result:
                return result["generated_text"]
            elif isinstance(result, str):
                return result
        except Exception as e:
            last_error = f"{model_id}: {e}"
    return None

def is_coding_question(text):
    # Basic heuristic: mentions code, function, "python", code blocks, etc.
    code_terms = [
        "python", "java", "c++", "code", "function", "write a", "script", "algorithm",
        "bug", "traceback", "error", "output", "compile", "debug"
    ]
    if any(term in text.lower() for term in code_terms):
        return True
    if re.search(r"```.+```", text, re.DOTALL):
        return True
    return False

def grok_completion(question, system_prompt=None):
    url = "https://api.x.ai/v1/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {GROK_API_KEY}"
    }
    payload = {
        "messages": [
            {"role": "system", "content": system_prompt or "You are a helpful coding and research assistant."},
            {"role": "user", "content": question}
        ],
        "model": "grok-3-latest",
        "stream": False,
        "temperature": 0
    }
    try:
        r = requests.post(url, headers=headers, json=payload, timeout=45)
        r.raise_for_status()
        data = r.json()
        # Extract assistant's reply
        return data['choices'][0]['message']['content']
    except Exception as e:
        return None

# ==== SMART AGENT ====
class SmartAgent:
    def __init__(self):
        pass

    def __call__(self, question: str) -> str:
        # 1. Handle file/link
        links = extract_links(question)
        if links:
            results = []
            for url in links:
                if re.search(r"\.xlsx|\.xls|\.csv|\.pdf|\.txt", url):
                    local = download_file(url)
                    if local:
                        file_analysis = analyze_file(local)
                        results.append(f"File ({url}):\n{file_analysis}")
                else:
                    results.append(analyze_webpage(url))
            if results:
                return "\n\n".join(results)

        # 2. Coding or algorithmic problems? Try Grok FIRST
        if is_coding_question(question):
            grok_response = grok_completion(question)
            if grok_response:
                return f"[Grok] {grok_response}"

        # 3. DuckDuckGo for web knowledge
        result = duckduckgo_search(question)
        if result:
            return result
        # 4. Wikipedia for encyclopedic queries
        result = wikipedia_search(question)
        if result:
            return result
        # 5. Grok again for hard/reasoning/general (if not already tried)
        if not is_coding_question(question):
            grok_response = grok_completion(question)
            if grok_response:
                return f"[Grok] {grok_response}"

        # 6. Fallback to LLM conversational
        result = llm_conversational(question)
        if result:
            return result
        return "No answer could be found by available tools."

# ==== 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

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

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

    try:
        response = requests.get(questions_url, timeout=15)
        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=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.')}"
        )
        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)