app.py

import os
import re
import json
import gradio as gr
import requests
import pandas as pd
from urllib.parse import quote
from bs4 import BeautifulSoup
from dotenv import load_dotenv

load_dotenv()

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
HF_TOKEN = os.getenv("HF_TOKEN", "")
REACT_MAX_STEPS = 10
LLM_MODEL = "Qwen/Qwen2.5-7B-Instruct"


# --- Tools (DuckDuckGo search, web page view, code agent) ---
def tool_web_search(query: str, max_results: int = 5) -> str:
    """Search the web using DuckDuckGo. Input: search query string."""
    try:
        from duckduckgo_search import DDGS
        results = list(DDGS().text(query, max_results=max_results))
        if not results:
            return "No search results found."
        out = []
        for i, r in enumerate(results, 1):
            out.append(f"{i}. {r.get('title', '')}\n   URL: {r.get('href', '')}\n   {r.get('body', '')}")
        return "\n\n".join(out)
    except Exception as e:
        return f"Web search error: {e}"


def tool_web_page_view(url: str) -> str:
    """View the main text content of a web page. Input: full URL string."""
    try:
        headers = {"User-Agent": "Mozilla/5.0 (compatible; ReActAgent/1.0)"}
        r = requests.get(url, timeout=15, headers=headers)
        r.raise_for_status()
        soup = BeautifulSoup(r.text, "html.parser")
        for tag in soup(["script", "style", "nav", "footer", "header"]):
            tag.decompose()
        text = soup.get_text(separator="\n", strip=True)
        return text[:8000] if len(text) > 8000 else text or "No text content found."
    except Exception as e:
        return f"Web page view error: {e}"


def tool_code_agent(code: str) -> str:
    """Run Python code to compute an answer. Input: a single Python expression or block (e.g. print(2+2)). No file or network access."""
    import builtins
    import io
    import sys
    safe_builtins = {
        "abs": builtins.abs, "all": builtins.all, "any": builtins.any,
        "bin": builtins.bin, "bool": builtins.bool, "chr": builtins.chr,
        "dict": builtins.dict, "divmod": builtins.divmod, "enumerate": builtins.enumerate,
        "filter": builtins.filter, "float": builtins.float, "format": builtins.format,
        "hash": builtins.hash, "int": builtins.int, "len": builtins.len,
        "list": builtins.list, "map": builtins.map, "max": builtins.max,
        "min": builtins.min, "next": builtins.next, "pow": builtins.pow,
        "print": builtins.print, "range": builtins.range, "repr": builtins.repr,
        "reversed": builtins.reversed, "round": builtins.round, "set": builtins.set,
        "sorted": builtins.sorted, "str": builtins.str, "sum": builtins.sum,
        "tuple": builtins.tuple, "zip": builtins.zip,
    }
    try:
        code = code.strip()
        if not code.startswith("print(") and "print(" not in code:
            code = f"print({code})"
        buf = io.StringIO()
        old_stdout = sys.stdout
        sys.stdout = buf
        try:
            exec(code, {"__builtins__": safe_builtins, "print": builtins.print}, {})
        finally:
            sys.stdout = old_stdout
        return buf.getvalue().strip() or "Code ran (no printed output)."
    except Exception as e:
        return f"Code error: {e}"


TOOLS = {
    "web_search": tool_web_search,
    "web_page_view": tool_web_page_view,
    "code_agent": tool_code_agent,
}

TOOL_DESCRIPTIONS = """Available tools:
- web_search: search the web with DuckDuckGo. Input: search query (string).
- web_page_view: get main text from a web page. Input: URL (string).
- code_agent: run Python code (math, string ops). Input: code (string)."""


# --- ReAct Agent: Plan -> Act -> Observe -> Reflect ---
class ReActAgent:
    def __init__(self, token: str | None = None, model: str = LLM_MODEL, max_steps: int = REACT_MAX_STEPS):
        self.token = (token or HF_TOKEN or "").strip()
        self.model = model
        self.max_steps = max_steps
        print("ReActAgent initialized (plan -> act -> observe -> reflect).")

    def _llm(self, messages: list[dict]) -> str:
        if not self.token:
            return "Error: HF_TOKEN not set. Add your token in .env to use the LLM."
        url = f"https://api-inference.huggingface.co/models/{self.model}"
        headers = {"Authorization": f"Bearer {self.token}", "Content-Type": "application/json"}
        payload = {"inputs": self._messages_to_prompt(messages), "parameters": {"max_new_tokens": 512, "return_full_text": False}}
        try:
            r = requests.post(url, json=payload, headers=headers, timeout=60)
            r.raise_for_status()
            data = r.json()
            if isinstance(data, list) and len(data) > 0:
                return (data[0].get("generated_text") or "").strip()
            if isinstance(data, dict) and "generated_text" in data:
                return (data["generated_text"] or "").strip()
            return ""
        except Exception as e:
            return f"LLM error: {e}"

    def _messages_to_prompt(self, messages: list[dict]) -> str:
        out = []
        for m in messages:
            role = m.get("role", "user")
            content = m.get("content", "")
            if role == "system":
                out.append(f"System: {content}")
            elif role == "user":
                out.append(f"User: {content}")
            else:
                out.append(f"Assistant: {content}")
        out.append("Assistant:")
        return "\n\n".join(out)

    def _parse_action(self, text: str) -> tuple[str | None, str | None, str | None]:
        """Returns (thought, action, action_input) or (None, None, final_answer)."""
        text = text.strip()
        final_match = re.search(r"Final Answer\s*:\s*(.+?)(?=\n\n|\Z)", text, re.DOTALL | re.IGNORECASE)
        if final_match:
            return None, None, final_match.group(1).strip()
        action_match = re.search(r"Action\s*:\s*(\w+)", text, re.IGNORECASE)
        input_match = re.search(r"Action Input\s*:\s*(.+?)(?=\n\n|\nThought:|\Z)", text, re.DOTALL | re.IGNORECASE)
        thought = None
        thought_match = re.search(r"Thought\s*:\s*(.+?)(?=\nAction:|\Z)", text, re.DOTALL | re.IGNORECASE)
        if thought_match:
            thought = thought_match.group(1).strip()
        action = action_match.group(1).strip() if action_match else None
        action_input = input_match.group(1).strip() if input_match else None
        if action_input:
            action_input = action_input.strip().strip('"\'')
        return thought, action, action_input

    def __call__(self, question: str) -> str:
        print(f"ReAct agent received question (first 50 chars): {question[:50]}...")
        if not self.token:
            return "HF_TOKEN not set. Add your Hugging Face token in .env to run the ReAct agent."
        system = (
            "You are a ReAct agent. For each turn you must either:\n"
            "1. Output: Thought: <reasoning> then Action: <tool_name> then Action Input: <input>\n"
            "2. Or when you have the answer: Final Answer: <your answer>\n\n"
            + TOOL_DESCRIPTIONS
        )
        messages = [
            {"role": "system", "content": system},
            {"role": "user", "content": f"Question: {question}\n\nFirst, plan which tool(s) to use, then take action, then observe, then reflect. Give your final answer when done."},
        ]
        for step in range(self.max_steps):
            response = self._llm(messages)
            thought, action, action_input = self._parse_action(response)
            if thought is None and action is None and action_input is not None:
                return action_input  # Final Answer
            if not action or action not in TOOLS:
                messages.append({"role": "assistant", "content": response})
                messages.append({"role": "user", "content": "You must use one of the tools (Action: tool_name, Action Input: input) or give Final Answer: your answer. Try again."})
                continue
            try:
                observation = TOOLS[action](action_input)
            except Exception as e:
                observation = f"Tool error: {e}"
            observation = (observation[:3000] + "...") if len(observation) > 3000 else observation
            messages.append({"role": "assistant", "content": response})
            messages.append({"role": "user", "content": f"Observation: {observation}\n\nReflect: does this answer the question? If yes, reply with Final Answer: <answer>. If not, use another tool (Thought / Action / Action Input)."})
        last_assistant = next((m["content"] for m in reversed(messages) if m.get("role") == "assistant"), "")
        final = self._parse_action(last_assistant)
        if final[2] and final[0] is None and final[1] is None:
            return final[2]
        return last_assistant[:500] if last_assistant else "ReAct agent reached max steps without a final answer."

def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code

    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"

    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = ReActAgent(token=os.getenv("HF_TOKEN"), max_steps=REACT_MAX_STEPS)
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # 2. Fetch Questions
    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

    # 3. Run your Agent
    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")
        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)
            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)

    # 4. Prepare Submission 
    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)

    # 5. Submit
    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)