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, Graph
# from langgraph.prebuilt import tools_condition
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain.agents import initialize_agent, AgentType
# from langchain_community.llms import HuggingFaceHub
# from langchain_community.chat_models import ChatHuggingFace
# import openai
# from openai import OpenAI
# from langchain_openai import ChatOpenAI

## # 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')
# OPENAI_MODEL =  os.getenv('OPENAI_MODEL') #'gpt-3.5-turbo-0613'
# OPENAI_KEY = os.getenv('OPENAI_KEY')
# client = OpenAI(api_key = OPENAI_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(input: str) -> str:
    """Get current UTC time as HH:MM."""
    return datetime.datetime.utc().strftime("%H:%M")

@tool
def get_date(input: 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 9: Image Captioning Tool ---
@tool
def image_caption(image_url: str) -> str:
    """Generate a descriptive caption for an image given its URL."""
    api_url = "https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-base"
    headers = {"Authorization": f"Bearer {HF_ACCESS_KEY}"}
    payload = {"inputs": image_url}
    try:
        resp = requests.post(api_url, headers=headers, json=payload, timeout=30)
        resp.raise_for_status()
        data = resp.json()
        return data[0]["generated_text"] if isinstance(data, list) else data.get("generated_text", "no_caption")
    except Exception:
        return "error"
    
# --- TOOL 10: Optical Character Recognition (OCR) Tool ---
@tool
def ocr_image(image_url: str) -> str:
    """Extract text from an image given its URL."""
    api_url = "https://api-inference.huggingface.co/models/impira/layoutlm-document-qa"
    headers = {"Authorization": f"Bearer {HF_ACCESS_KEY}"}
    payload = {"inputs": {"image": image_url, "question": "What text is in the image?"}}
    try:
        resp = requests.post(api_url, headers=headers, json=payload, timeout=30)
        resp.raise_for_status()
        data = resp.json()
        return data.get("answer", "no_text_found")
    except Exception:
        return "error"
    
# --- TOOL 11: Image Classification Tool ---
@tool
def classify_image(image_url: str) -> str:
    """Classify the main object or scene in an image given its URL."""
    api_url = "https://api-inference.huggingface.co/models/google/vit-base-patch16-224"
    headers = {"Authorization": f"Bearer {HF_ACCESS_KEY}"}
    payload = {"inputs": image_url}
    try:
        resp = requests.post(api_url, headers=headers, json=payload, timeout=30)
        resp.raise_for_status()
        data = resp.json()
        return data[0]["label"] if isinstance(data, list) else data.get("label", "no_label")
    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,
    image_caption,
    ocr_image,
    classify_image
]

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, you must do your internal reasoning using the Thought → Action → Observation → Answer process, but your output to the user should be ONLY the final answer as a single value (number, string, or comma-separated list), with no extra explanation, thoughts, actions, or observations.

**Your output must be only the answer. Do not include any reasoning, tool calls, or explanations.**

Examples:

Q: What is 7 * (3 + 2)?
Your Output: 35

Q: What’s the weather in Tokyo?
Your Output: 22

Q: What is the capital of France?
Your Output: Paris

Q: Convert 10 meters to feet.
Your Output: 32.81

Instructions:
- Always do your internal reasoning (Thought → Action → Observation → Answer) before producing the answer, but DO NOT show this reasoning to the user.
- 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.
- Your output must be a single value (number, string, or comma-separated list) with no extra explanation or formatting.
- 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_llm = ChatHuggingFace(llm=llm)
'''
llm = ChatOpenAI(
    openai_api_key=OPENAI_KEY,
    model_name=OPENAI_MODEL,
    temperature=0.1
)
'''
# chat = ChatHuggingFace(llm=llm, verbose=True)
# tools = [search_tool, fetch_weather]
# chat_with_tools = chat.bind_tools(tools)

agent = initialize_agent(
    tools=tools_list,
    llm=llm,
    # llm=chat_llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    # agent_kwargs={"system_message": system_prompt},
    verbose=True,
    handle_parsing_errors=True
)


## --
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:
            full_prompt = f"{system_prompt}\n Input Question: {question_text}"
            submitted_answer = agent.run(full_prompt)
            # submitted_answer = agent.run(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()
    # 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...")

# Launch the Gradio app
demo.launch(debug=True, share=True) #share=True