app.py

import os
import gradio as gr
import requests
# import math
import inspect
import pandas as pd
import datetime
# from dotenv import load_dotenv

from langchain.tools import tool
# from langchain_community.tools import get_all_tools
from typing import TypedDict, Annotated
# from langgraph.graph.message import add_messages
# from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
from langgraph.prebuilt import ToolNode
from langgraph.graph import START, StateGraph, END
# from langgraph.prebuilt import tools_condition
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace

## # Load environment variables from .env file
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# Load the environment variables
# load_dotenv()
HF_ACCESS_KEY = os.getenv('HF_ACCESS_KEY')
WEATHER_API_KEY = os.getenv('WEATHER_API_KEY')

########## ----- DEFINING TOOLS -----##########

# --- TOOL 1: Web Search Tool (DuckDuckGo) ---
@tool
def search_tool(query: str) -> str:
    """Answer general knowledge or current events queries using DuckDuckGo."""
    url = f"https://api.duckduckgo.com/?q={query}&format=json&no_html=1"
    try:
        resp = requests.get(url, timeout=20)
        resp.raise_for_status()
        data = resp.json()
        for key in ["AbstractText", "Answer", "Definition"]:
            if data.get(key):
                return data[key].split(".")[0]
        return "no_answer"
    except Exception:
        return "error"

# when you use the @tool decorator from langchain.tools, the tool.name and tool.description are automatically extracted from your function
# tool.name is set to the function name (e.g., `search_tool`), and 
# tool.description is set to the docstring of the function  (the triple-quoted string right under def ...) (e.g., "Answer general knowledge or current events queries using DuckDuckGo.").

# --- TOOL 2: Weather Tool (OpenWeatherMap) ---
@tool
def get_weather(city: str) -> str:
    """Get current temperature in Celsius for a city."""
    import os
    api_key = os.environ.get("WEATHER_API_KEY")
    url = f"https://api.openweathermap.org/data/2.5/weather?q={city}&appid={WEATHER_API_KEY}&units=metric"
    try:
        resp = requests.get(url, timeout=20)
        resp.raise_for_status()
        data = resp.json()
        return str(round(data["main"]["temp"]))
    except Exception:
        return "error"

# --- TOOL 3: Calculator Tool ---
@tool
def calculator(expression: str) -> str:
    """Evaluate math expressions."""
    try:
        allowed = "0123456789+-*/(). "
        if not all(c in allowed for c in expression):
            return "error"
        result = eval(expression, {"__builtins__": None}, {})
        return str(result)
    except Exception:
        return "error"
    
# --- TOOL 4: Unit Conversion Tool ---
@tool
def convert_units(args: str) -> str:
    """
    Convert between metric and imperial units (length, mass, temperature).
    Input format: '<value> <from_unit> to <to_unit>', e.g. '10 meters to feet'
    """
    try:
        parts = args.lower().split()
        value = float(parts[0])
        from_unit = parts[1]
        to_unit = parts[3]
        conversions = {
            ("meters", "feet"): lambda v: v * 3.28084,
            ("feet", "meters"): lambda v: v / 3.28084,
            ("kg", "lb"): lambda v: v * 2.20462,
            ("lb", "kg"): lambda v: v / 2.20462,
            ("celsius", "fahrenheit"): lambda v: v * 9/5 + 32,
            ("fahrenheit", "celsius"): lambda v: (v - 32) * 5/9,
        }
        func = conversions.get((from_unit, to_unit))
        if func:
            return str(round(func(value), 2))
        return "error"
    except Exception:
        return "error"

# --- TOOL 5: Date & Time Tool ---
@tool
def get_time(_: str = "") -> str:
    """Get current UTC time as HH:MM."""
    return datetime.datetime.utc().strftime("%H:%M")

@tool
def get_date(_: str = "") -> str:
    """Get current date as YYYY-MM-DD."""
    return datetime.datetime.utc().strftime("%Y-%m-%d")

# --- TOOL 6: Wikipedia Summary Tool ---
@tool
def wikipedia_summary(query: str) -> str:
    """Get a short summary of a topic from Wikipedia."""
    url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{query.replace(' ', '_')}"
    try:
        resp = requests.get(url, timeout=20)
        resp.raise_for_status()
        data = resp.json()
        return data.get("extract", "no_answer").split(".")[0]
    except Exception:
        return "error"

# --- TOOL 7: Dictionary Tool ---
@tool
def dictionary_lookup(word: str) -> str:
    """Get the definition of an English word."""
    url = f"https://api.dictionaryapi.dev/api/v2/entries/en/{word}"
    try:
        resp = requests.get(url, timeout=20)
        resp.raise_for_status()
        data = resp.json()
        return data[0]["meanings"][0]["definitions"][0]["definition"]
    except Exception:
        return "error"
    
# --- TOOL 8: Currency Conversion Tool ---
@tool
def currency_convert(args: str) -> str:
    """
    Convert an amount from one currency to another.
    Input format: '<amount> <from_currency> to <to_currency>', e.g. '100 USD to EUR'
    """
    try:
        parts = args.upper().split()
        amount = float(parts[0])
        from_currency = parts[1]
        to_currency = parts[3]
        url = f"https://api.exchangerate.host/convert?from={from_currency}&to={to_currency}&amount={amount}"
        resp = requests.get(url, timeout=20)
        resp.raise_for_status()
        data = resp.json()
        return str(round(data["result"], 2))
    except Exception:
        return "error"

##-- Tool Discovery ---
# Use @tool for each function.
# Use get_all_tools() to auto-discover all decorated tools.
# tools_list = get_all_tools()
tools_list = [
    search_tool,
    get_weather,
    calculator,
    convert_units,
    get_time,
    get_date,
    wikipedia_summary,
    dictionary_lookup,
    currency_convert,
]

tool_descriptions = "\n".join(f"- {tool.name}: {tool.description}" for tool in tools_list)


## --
# --- System Prompt for the Agent ---
system_prompt = f"""
You are an intelligent assistant with access to the following tools:

{tool_descriptions}

For every question, always follow this process (your internal thinking/execution process):

1. Thought: Reflect step by step on what the user is asking and what information or calculation is needed. Decide if you need to use a tool or can answer directly.
2. Action: If a tool is needed, specify which tool to use and with what input. If not, state "No action needed".
3. Observation: If you used a tool, report the tool's output here. If not, write "N/A".
4. Answer: Give the final answer as a single value (number, string, or comma-separated list), with no extra explanation or units unless requested.

Your Final Answer should be just [Answer] and should not include any additional text or explanation. Final Answer should be a single value (number, string, or comma-separated list).

Examples:

Q: What is 7 * (3 + 2)?
Thought: The user is asking for a math calculation. I should use the calculator tool.
Action: calculator("7 * (3 + 2)")
Observation: 35
Answer: 35

Your Output (Final Answer) for this question should be: '35'.

Q: What’s the weather in Tokyo?
Thought: The user wants the current temperature in Tokyo. I should use the get_weather tool.
Action: get_weather("Tokyo")
Observation: 22
Answer: 22

Your Output (Final Answer) for this question should be: '22'.

Q: What is the capital of France?
Thought: The user is asking for a factual answer. I can answer directly.
Action: No action needed
Observation: N/A
Answer: Paris

Your Output (Final Answer) for this question should be: 'Paris'.

Q: Convert 10 meters to feet.
Thought: The user wants to convert units. I should use the convert_units tool.
Action: convert_units("10 meters to feet")
Observation: 32.81
Answer: 32.81

Your Output (Final Answer) for this question should be: '32.81'.

Instructions:
- Always follow the Thought → Action → Observation → Answer for your internal reasoning and execution before giving final answer.
- Use a tool only if necessary, and don't use multiple tools in a call. Don't use a tool if you can answer directly without hallucination. 
- Always return your final answer as a single value, with no extra explanation.
- Be concise and accurate.
"""

## --- Initialize Hugging Face Model ---
# Generate the chat interface, including the tools
llm = HuggingFaceEndpoint(
    repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
    huggingfacehub_api_token=HF_ACCESS_KEY,
    model_kwargs={'prompt': system_prompt}
    # system_prompt=system_prompt,
)
# chat = ChatHuggingFace(llm=llm, verbose=True)
# tools = [search_tool, fetch_weather]
# chat_with_tools = chat.bind_tools(tools)


##
# --- LANGGRAPH AGENT SETUP ---

# Define the state for the graph
class AgentState(dict):
    pass

# Define the main node (agent logic)
def agent_node(state: AgentState) -> AgentState:
    question = state["question"]
    # The LLM will decide which tool to use based on the prompt and tools
    # response = chat_with_tools.invoke(question) # use this if using ChatHuggingFace with binding option to tools
    response = llm.invoke(question, tools=tools_list)
    return AgentState({"question": question, "answer": response})

# Build the graph
graph = StateGraph(AgentState)
graph.add_node("agent", agent_node)
# graph.add_node("tools", ToolNode(tools)) #use this when using ChatHuggingFace with binding option to tools
# graph.add_edge(START, "agent") #alternatively use the below with set_entry_point
graph.set_entry_point("agent")
graph.add_edge("agent", END)
my_agent = graph.compile()

# Or try simply with Graph instead of StateGraph
# from langgraph.graph import Graph
# graph = Graph(llm=llm, tools=tools_list)
# def agent(question: str) -> str:
#     return graph.run(question)

## --- AGENT CALL FUNCTION ---
def agent(question: str) -> str:
    state = AgentState({"question": question})
    result = my_agent.invoke(state)
    return result["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 = 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


# --- 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.
        """
    )

    login_btn = gr.LoginButton()
    login_btn.activate()

    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=True)