app.py

import os
import gradio as gr
import requests
import pandas as pd
from huggingface_hub import InferenceClient
from duckduckgo_search import DDGS
from datasets import load_dataset
import wikipediaapi
from llama_index.core import VectorStoreIndex, Document, StorageContext, load_index_from_storage
from llama_index.llms.huggingface import HuggingFaceLLM

DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
HF_TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN")
llm_model_id = "deepseek-ai/DeepSeek-V2"
llm_fallback_id = "mistralai/Mistral-7B-Instruct-v0.2"

# Setup HF LLM client
hf_client = InferenceClient(llm_model_id, token=HF_TOKEN)
hf_fallback = InferenceClient(llm_fallback_id, token=HF_TOKEN)

# Wikipedia API with user agent
wiki_api = wikipediaapi.Wikipedia(
    language='en',
    user_agent='SmartAgent/1.0 ([email protected])'
)

# Build or load LlamaIndex for fast retrieval (optional, for small Wikipedia sample)
try:
    wiki_dataset = load_dataset("wikipedia", "20220301.en", split="train[:5000]", trust_remote_code=True)
    docs = [Document(text=doc['text']) for doc in wiki_dataset]
    index = VectorStoreIndex.from_documents(docs)
except Exception as e:
    index = None

def duckduckgo_search(query):
    with DDGS() as ddgs:
        results = [r for r in ddgs.text(query, max_results=3)]
        return "\n".join([r["body"] for r in results if r.get("body")]) or "No results found."

def wikipedia_search(query):
    page = wiki_api.page(query)
    return page.summary if page.exists() else None

def index_search(query):
    if index is None:
        return None
    res = index.as_query_engine().query(query)
    return str(res) if res else None

def handle_excel(file_url):
    # Download and sum food (not drinks)
    try:
        fname = "tmp.xlsx"
        r = requests.get(file_url)
        with open(fname, "wb") as f:
            f.write(r.content)
        df = pd.read_excel(fname)
        # Assume drinks have 'drink' or 'beverage' in a column called 'Item' or 'Category'
        if "Item" in df.columns:
            food_df = df[~df["Item"].str.contains("drink|beverage", case=False, na=False)]
            total = food_df["Total"].sum()
            return f"${total:.2f}"
        if "Category" in df.columns:
            food_df = df[df["Category"].str.lower() == "food"]
            total = food_df["Total"].sum()
            return f"${total:.2f}"
        return "File parsed but could not find food sales."
    except Exception as e:
        return f"Excel error: {e}"

class SmartAgent:
    def __init__(self):
        pass

    def __call__(self, question: str) -> str:
        q_lower = question.lower()

        # DuckDuckGo for current events/recent/live
        if any(term in q_lower for term in ["current", "latest", "2024", "2025", "who is the president", "recent", "live"]):
            return duckduckgo_search(question)

        # Wikipedia summary
        wiki_result = wikipedia_search(question)
        if wiki_result:
            return wiki_result

        # LlamaIndex retrieval
        rag_result = index_search(question)
        if rag_result:
            return rag_result

        # LLM generation
        try:
            resp = hf_client.text_generation(question, max_new_tokens=256)
            return resp
        except Exception:
            try:
                resp = hf_fallback.text_generation(question, max_new_tokens=256)
                return resp
            except Exception as e:
                return f"HF LLM error: {e}"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")
    if profile:
        username = profile.username
        print(f"User logged in: {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")
        file_url = item.get("file_url", None)
        if not task_id or not question_text:
            continue

        # Handle Excel task
        if file_url and ("excel" in question_text.lower() or "file" in question_text.lower()):
            submitted_answer = handle_excel(file_url)
        else:
            submitted_answer = agent(question_text)

        # Final answer extraction/formatting if needed (TODO: Add regex/extract logic)
        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}
    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.')}"
        )
        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 interface
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)