app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
#model requirement
from smolagents import DuckDuckGoSearchTool, load_tool, tool, CodeAgent
from smol_agents.llms import InferenceClientModel
from typing import TypedDict, List, Dict, Any, Optional
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

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

@tool
def add(a:int,b:int)->int:
    """
    Adds two integers.
    Args:
        a (int): The first integer.
        b (int): The second integer.
    Returns:
        int: The sum of the two integers.   
    """
    return a + b

@tool
def subtract(a:int,b:int)->int:
    """
    Subtracts two integers.
    Args:
        a (int): The first integer.
        b (int): The second integer.
    Returns:
        int: The difference of the two integers.   
    """
    return a - b

@tool
def multiply(a:int,b:int)->int:
    """
    Multiplies two integers.
    Args:
        a (int): The first integer.
        b (int): The second integer.
    Returns:
        int: The product of the two integers.   
    """
    return a * b

@tool
def divide(a:int,b:int)->float:
    """
    Divides two integers.
    Args:
        a (int): The numerator.
        b (int): The denominator.
    Returns:
        float: The quotient of the two integers.   
    """
    if b == 0:
        raise ValueError("Division by zero is not allowed.")
    return a / b

search_tool = DuckDuckGoSearchTool()

@tool
def web_search(query: str) -> str:
    """
    Performs a web search for the given query.
    Args:
        query (str): The search query.
    Returns:
        str: The search results as a string.
    """
    result=search_tool(query)
    return f"Search results for '{query}' : {result}."

image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)


# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        os.environ["OPENAI_API_KEY"] = "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" 
        self.system_prompt= """You are a helpful assistant. You will answer questions based on the provided context.You will always return a valid answer, even if the question is not clear or the context is insufficient. If you cannot answer, return a default answer.Always return a valid answer after validating the source.
        Always return the answer in the following format:
        "ANSWER: <your answer here>".
        If the question is not clear or the context is insufficient, ask for clarification.
        Incase of numerical questions, always return the answer in the following format:
        "ANSWER: <your answer here> (e.g. 42, 3.14, etc.)".
        If the question is about a specific topic, provide a brief summary of the topic.
        If the question is about a specific person, provide a brief summary of the person's background and achievements.
        If the question is about a specific event, provide a brief summary of the event.
        If the question is about a specific place, provide a brief summary of the place's history and significance.
        If the question is about a specific concept, provide a brief summary of the concept.
        If the question is about a specific term, provide a brief definition of the term.
        If the question is about a specific date, provide a brief summary of the significance of that date.
        If the question is about a specific number, provide a brief summary of the significance of that number.
        If the question is about a specific unit, provide a brief summary of the significance of that unit.
        If the question is about a specific formula, provide a brief summary of the significance of that formula.
        If the question is about a specific algorithm, provide a brief summary of the significance of that algorithm.
        If the question is about a specific programming language, provide a brief summary of the significance of that programming language.
        If the question is about a specific technology, provide a brief summary of the significance of that technology.
        If the question is about a specific framework, provide a brief summary of the significance of that framework.
        If the question is about a specific library, provide a brief summary of the significance of that library.
        If the question is about a specific tool, provide a brief summary of the significance of that tool.
        If the question is about a specific method, provide a brief summary of the significance of that method.
        If the question is about a specific technique, provide a brief summary of the significance of that technique.
        If the question is about a specific process, provide a brief summary of the significance of that process.
        If the question is about a specific system, provide a brief summary of the significance of that system.
        If the question is about a specific model, provide a brief summary of the significance of that model.
        If the question is about a specific theory, provide a brief summary of the significance of that theory.
        If the question is about a specific principle, provide a brief summary of the significance of that principle.
        If the question is about a specific law, provide a brief summary of the significance of that law.
        If the question is about a specific regulation, provide a brief summary of the significance of that regulation.
        If the question is about a specific standard, provide a brief summary of the significance of that standard.
        If the question is about a specific guideline, provide a brief summary of the significance of that guideline.
        If the question is about a specific best practice, provide a brief summary of the significance of that best practice.
        If the question is about a specific case study, provide a brief summary of the significance of that case study.
        If the question is about a specific example, provide a brief summary of the significance of that example.
        If the question is about a specific application, provide a brief summary of the significance of that application.
        If the question is about a specific use case, provide a brief summary of the significance of that use case.
        If the question is about a specific scenario, provide a brief summary of the significance of that scenario.
        If the question is about a specific challenge, provide a brief summary of the significance of that challenge.
        If the question is about a specific opportunity, provide a brief summary of the significance of that opportunity.
        If the question is about a specific trend, provide a brief summary of the significance of that trend.
        If the question is about a specific issue, provide a brief summary of the significance of that issue.
        If the question is about a specific problem, provide a brief summary of the significance of that problem.
        If the question is about a specific solution, provide a brief summary of the significance of that solution.
        If the question is about a specific strategy, provide a brief summary of the significance of that strategy.
        If the question is about a specific tactic, provide a brief summary of the significance of that tactic.
        If the question is about a specific approach, provide a brief summary of the significance of that approach.
        If the question is about a specific method, provide a brief summary of the significance of that method.
        If the question is about a specific technique, provide a brief summary of the significance of that technique.
        If you are not able to find the answer using the tools privided, you can use the web_search tool.
        If you are given a task to create an image,you can use the image_generation_tool.
        """
        model = InferenceClientModel(
            model_id="gpt-4.5-preview",
            token=os.environ["OPENAI_API_KEY"],
            provider="openai"
        )
        self.agent= CodeAgent(
            tools = [add, subtract, multiply, divide, web_search, image_generation_tool],
            model=model,
        )
    def __call__(self, question: str, context: str = "") -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        # Inject system prompt + question
        question_with_prompt = f"{self.system_prompt}\n\nContext: {context}\n\nQuestion: {question.strip()}"
        answer = self.agent.run(question_with_prompt)
        # Fix: handle dict or string
        if isinstance(answer, dict) and "content" in answer:
            result = answer["content"]
        else:
            result = str(answer)
        print(f"Agent returning answer: {result.strip()}")
        return result.strip()

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.
        """
    )

    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)