app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import re
import math
import json
import unicodedata
from typing import TypedDict, Annotated, Any, List, Optional
 
from huggingface_hub import InferenceClient
 
from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage, AIMessage, ToolMessage
from langchain_core.tools import tool
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.utilities import WikipediaAPIWrapper
 
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition

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

@tool
def web_search(query: str) -> str:
    """Search the web using DuckDuckGo for current facts, news, specific data, recent information, and verification. Returns top search results."""
    try:
        return DuckDuckGoSearchRun().run(query)
    except Exception as e:
        return f"Search error: {e}"
 
 
def _wikipedia_api_query(title: str) -> str:
    """Get plain text extract from English Wikipedia for a page title."""
    import urllib.parse
    url = (
        "https://en.wikipedia.org/w/api.php"
        "?action=query&format=json&prop=extracts&explaintext=1&titles="
        + urllib.parse.quote(title)
    )
    headers = {"User-Agent": "Mozilla/5.0 (compatible; AgentBot/1.0; +https://example.com/bot)"}
    r = requests.get(url, timeout=15, headers=headers)
    try:
        r.raise_for_status()
    except requests.exceptions.HTTPError as ee:
        print(f"Warning: Wikipedia API HTTP error {ee}")
        return ""
    data = r.json()
    pages = data.get("query", {}).get("pages", {})
    if not pages:
        return ""
    text = next(iter(pages.values())).get("extract", "")
    return text or ""


@tool
def wikipedia_api_query(title: str) -> str:
    """Get plain text extract from English Wikipedia for a page title."""
    return _wikipedia_api_query(title)


@tool
def wikipedia_search(query: str) -> str:
    """Search Wikipedia for encyclopedic knowledge: historical facts, biographies, dates, definitions, figures, scientific information. Provides structured text summaries."""
    try:
        wiki = WikipediaAPIWrapper(top_k_results=3, doc_content_chars_max=3000)
        return wiki.run(query)
    except Exception as e:
        return f"Wikipedia error: {e}"
 
 
@tool
def python_repl(code: str) -> str:
    """
    Execute Python code for mathematical calculations, data processing, logic operations, and transformations.
    Always use print() to output results. 
    Examples: print(2**10), print([1,2,3].count(2)), data=[1,2,3]; print(sum(data)/len(data))
    """
    import io, sys
    old_stdout = sys.stdout
    sys.stdout = io.StringIO()
    try:
        exec(code, {"math": math, "json": json, "re": re,
                    "unicodedata": unicodedata, "__builtins__": __builtins__})
        output = sys.stdout.getvalue()
        return output.strip() if output.strip() else "Code executed with no output. Use print()."
    except Exception as e:
        return f"Code error: {e}"
    finally:
        sys.stdout = old_stdout
 
 
@tool
def calculator(expression: str) -> str:
    """
    Evaluate a mathematical expression quickly. Use for simple arithmetic and compound calculations.
    Examples: '2 + 2', '100 * 1.07 ** 5', 'math.sqrt(144)', '(50 + 30) / 2'
    """
    try:
        return str(eval(expression, {"math": math, "__builtins__": {}}))
    except Exception as e:
        return f"Calculation error: {e}"
 
 
@tool
def get_task_file(task_id: str) -> str:
    """
    Fetch the file or document attached to a GAIA task by its task_id.
    Use this when the question mentions an attached file, document, PDF, or any attachment.
    Returns text content for text/JSON files, or indicates binary file type.
    """
    try:
        import requests as req
        url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
        response = req.get(url, timeout=15)
        if response.status_code == 200:
            ct = response.headers.get("Content-Type", "")
            if "text" in ct or "json" in ct:
                return response.text[:5000]
            return f"[Binary file - content-type: {ct}]"
        return f"No file found for task {task_id}"
    except Exception as e:
        return f"Error fetching task file: {e}"
 


class AgentState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]

SYSTEM_PROMPT = """You are a highly capable GAIA benchmark solver. Your goal is to answer questions accurately and precisely.

## How to Solve Questions - Step by Step

1. **Understand the Question**: Read carefully and identify:
   - What type of answer is expected (number, text, list, date, etc.)
   - Key constraints or special formats mentioned
   - Whether a file or document is attached

2. **Choose Your Approach**:
   - For arithmetic/math: Use `calculator` or `python_repl`
   - For current facts/events: Use `web_search`
   - For historical/encyclopedic knowledge: Use `wikipedia_search`
   - For attached files: Use `get_task_file`
   - For complex logic/data processing: Use `python_repl`

3. **Use Tools Effectively**:
   - Search for key facts and verify information from multiple sources
   - Extract relevant data from search results
   - Perform calculations or transformations
   - Cross-check results when possible

4. **Format Your Final Answer**:
   - For numbers: just the number (e.g., "42", "3.14", "-5")
   - For text: exact text without extra punctuation (e.g., "Paris", "Monday")
   - For lists: comma-separated values (e.g., "item1, item2, item3")
   - For dates: use the format specified in the question
   - If completely unsure: respond with just "Unknown"

5. **End Response**:
   After your reasoning, output a clean final answer on a new line:
   FINAL ANSWER: <your answer>

## Important Rules
- Never make up facts - always search or calculate
- Verify key numbers and spelling with web search
- If a calculation is involved, always show the work
- Be concise in your reasoning but thorough in verification
"""

def _tool_to_openai_schema(t) -> dict:
    """Converte un LangChain tool nel formato tool OpenAI."""
    return {
        "type": "function",
        "function": {
            "name": t.name,
            "description": t.description,
            "parameters": t.args_schema.schema() if t.args_schema else {"type": "object", "properties": {}},
        }
    }

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        print("Initializing agent with HF InferenceClient...")
 
        self.tools_list = [
            web_search,
            wikipedia_search,
            python_repl,
            calculator,
            get_task_file,
        ]
 
        # Mappa nome → funzione tool per esecuzione
        self.tools_by_name = {t.name: t for t in self.tools_list}

        hf_token = os.getenv("HF_TOKEN")
        if not hf_token:
            print("WARNING: HF_TOKEN non impostata. L'agente userà fallback locale e risposte molto limitate.")
            self.client = None
        else:
            # InferenceClient diretto — usa la Serverless Inference API HF
            self.client = InferenceClient(
                api_key=hf_token,
            )
         
        # Schema OpenAI dei tool per passarli al client
        self.tools_schema = [_tool_to_openai_schema(t) for t in self.tools_list]
 
        # Grafo LangGraph per gestire il loop ReAct
        builder = StateGraph(AgentState)
        builder.add_node("assistant", self._assistant_node)
        builder.add_node("tools", ToolNode(self.tools_list))
        builder.add_edge(START, "assistant")
        builder.add_conditional_edges("assistant", tools_condition)
        builder.add_edge("tools", "assistant")
        self.graph = builder.compile()
 
        print("Agent ready.")
 
    def _messages_to_hf_format(self, messages: list) -> list:
        """Converte messaggi LangChain nel formato dict che InferenceClient si aspetta."""
        result = []
        for m in messages:
            if isinstance(m, SystemMessage):
                result.append({"role": "system", "content": m.content})
            elif isinstance(m, HumanMessage):
                result.append({"role": "user", "content": m.content})
            elif isinstance(m, AIMessage):
                msg = {"role": "assistant", "content": m.content or ""}
                # Includi tool_calls se presenti
                if m.tool_calls:
                    msg["tool_calls"] = [
                        {
                            "id": tc["id"],
                            "type": "function",
                            "function": {
                                "name": tc["name"],
                                "arguments": json.dumps(tc["args"]),
                            }
                        }
                        for tc in m.tool_calls
                    ]
                result.append(msg)
            elif isinstance(m, ToolMessage):
                result.append({
                    "role": "tool",
                    "tool_call_id": m.tool_call_id,
                    "content": m.content,
                })
        return result
 
    def _assistant_node(self, state: AgentState):
        """Nodo assistant: chiama InferenceClient con i tool e restituisce la risposta."""
        sys_msg = SystemMessage(content=SYSTEM_PROMPT)
        hf_messages = self._messages_to_hf_format([sys_msg] + state["messages"])
 
        response = self.client.chat_completion(
            model="Qwen/Qwen2.5-72B-Instruct",            
            messages=hf_messages,
            tools=self.tools_schema,
            tool_choice="auto",
            max_tokens=1000,
            temperature=0.1,
        )
 
        choice = response.choices[0].message
 
        # Costruisci AIMessage compatibile con LangGraph
        tool_calls = []
        if choice.tool_calls:
            for tc in choice.tool_calls:
                tool_calls.append({
                    "id": tc.id,
                    "name": tc.function.name,
                    "args": json.loads(tc.function.arguments),
                    "type": "tool_call",
                })
 
        ai_message = AIMessage(
            content=choice.content or "",
            tool_calls=tool_calls,
        )
        return {"messages": [ai_message]}

    def _local_fallback_answer(self, question: str) -> str:
        """
        Minimal fallback when inference client is unavailable.
        Attempts basic arithmetic only, otherwise returns Unknown.
        """
        q = question.lower().strip()
        
        # Try simple arithmetic if it looks like a math problem
        if re.search(r"(?:how\s+many|calculate|compute|what\s+is).*\d+", q):
            try:
                # Try to extract and evaluate a simple expression
                numbers = re.findall(r"\d+\.?\d*", question)
                if len(numbers) >= 2:
                    # Don't try to hardcode logic - just return Unknown
                    pass
            except Exception:
                pass

        return "Unknown"

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 100 chars): {question[:100]}...")

        if self.client is None:
            print("No HF InferenceClient configured; using local fallback logic")
            return self._local_fallback_answer(question)

        q_lower = question.lower().strip()

        # Numeric math shortcut: explicit arithmetic detection reduces hallucination.
        arithmetic = self._extract_arithmetic_expression(question)
        if arithmetic:
            calc = calculator(arithmetic)
            if not calc.startswith("Calculation error"):
                normalized = self._normalize_answer(calc)
                print(f"Arithmetic shortcut using calculator: {arithmetic} -> {normalized}")
                return normalized

        # The main RL loop
        answer = self._run_agent(question)

        # Retry with explicit “Unknown” handling and chain-of-thought guidance
        if answer == "Unknown":
            print("Got Unknown on first pass; retrying with more explicit reasoning request")
            replay_question = question + "\n\nPlease reason step by step with tool calls and provide FINAL ANSWER only."  # gentle prompt nudge
            answer = self._run_agent(replay_question)

        print(f"Agent returning answer: '{answer}'")
        return answer

    def _run_agent(self, question: str) -> str:
        try:
            result = self.graph.invoke({"messages": [HumanMessage(content=question)]})
            last_message = result["messages"][-1]
            response_text = last_message.content if isinstance(last_message, AIMessage) else str(last_message)
            print(f"Agent raw output (first 300 chars): {response_text[:300]}...")
            return self._extract_answer(response_text)
        except Exception as e:
            error_message = str(e)
            print(f"Agent error during run: {error_message}")
            if "402" in error_message or "Payment Required" in error_message:
                fallback = self._local_fallback_answer(question)
                print(f"Payment required detected; using local fallback answer: {fallback}")
                return fallback
            return "Unknown"

    def _extract_answer(self, text: str) -> str:
        """
        Extract the final answer from agent output using multiple strategies.
        """
        # Strategy 1: Look for explicit "FINAL ANSWER:" marker
        match = re.search(r"FINAL ANSWER:\s*(.+?)(?:\n|$)", text, re.IGNORECASE)
        if match:
            answer = self._normalize_answer(match.group(1).strip())
            if answer and answer != "Unknown":
                return answer

        # Strategy 2: Look at the last few lines
        lines = [line.strip() for line in text.split('\n') if line.strip()]
        if lines:
            for candidate in reversed(lines[-4:]):
                if candidate and not any(phrase in candidate.lower() for phrase in ["i'm not sure", "error", "failed", "final answer"]):
                    normalized = self._normalize_answer(candidate)
                    if normalized and normalized != "Unknown":
                        return normalized

        # Fallback
        return "Unknown"

    def _normalize_answer(self, answer: str) -> str:
        """Normalize answer text (strip punctuation, normalize choices etc.)."""
        answer_clean = answer.strip().strip('"\'').rstrip('.?,;')

        if not answer_clean:
            return "Unknown"

        # Multiple-choice token: take plain option text if present
        mc = re.match(r"^([A-D])\s*[:\)]\s*(.+)$", answer_clean, re.IGNORECASE)
        if mc:
            return mc.group(2).strip()

        # Numeric decision: enforce numeric format for numeric questions
        if re.match(r"^-?\d+(\.\d+)?$", answer_clean):
            return answer_clean

        return answer_clean

    def _extract_arithmetic_expression(self, question: str) -> Optional[str]:
        """Extract simple arithmetic expression candidate from a question for calculator use."""
        m = re.search(r"([-+]?\d+(?:\.\d+)?(?:\s*[-+*/]\s*\d+(?:\.\d+)?)+)", question)
        if not m:
            return None
        expr = m.group(1).replace("^", "**")
        if re.search(r"[a-zA-Z]", expr):
            return None
        return expr

    def _detect_question_type(self, question: str) -> str:
        q = question.lower().strip()
        if any(tok in q for tok in ["how many", "calculate", "compute", "sum", "difference", "times", "per cent", "%"]):
            return "numeric"
        if any(tok in q for tok in ["when", "year", "date", "born", "died"]):
            return "date"
        if any(tok in q for tok in ["which of the following", "option", "choose", "select"]):
            return "multiple_choice"
        return "factual"


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 = BasicAgent()
    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


def build_gradio_ui():
    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)
        results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

        run_button.click(
            fn=run_and_submit_all,
            outputs=[status_output, results_table]
        )

    return demo

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")

    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(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 = build_gradio_ui()
    demo.launch(debug=True, share=False)