app.py

import os
import gradio as gr
import requests
import pandas as pd
import base64

from dotenv import load_dotenv
from typing import TypedDict, Annotated
from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_ollama.chat_models import ChatOllama
from langchain.tools import Tool
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition

from huggingface_hub import login, InferenceClient

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

model_id = "google/gemma-3-4b-it"

client = InferenceClient(model=model_id)

def get_file_path(task_id: str, question) -> str:
    """Retrieves reference file path."""
    if question['task_id'] == task_id:
        return question['file_path']


def get_ref_content(path: str) -> str | object:
    """Retrieves content from the reference path provided."""
    with open(path, "rb") as f:
        file = f.read()
    return file


def search_web(topic: str) -> str:
    """Retrieves information about the topic."""
    results = DuckDuckGoSearchRun().invoke(topic)
    if results:
        return "\n\n".join([doc.text for doc in results[:2]])
    else:
        return "No matching content found."


def extract_text_from_image(img_path: str) -> str:
    """Extracts text from image"""
    try:
        # Read image and encode as base64
        with open(img_path, "rb") as image_file:
            image_bytes = image_file.read()

        image_base64 = base64.b64encode(image_bytes).decode("utf-8")
        return image_base64
    except Exception as e:
        # A butler should handle errors gracefully
        error_msg = f"Error extracting text: {str(e)}"
        print(error_msg)
        return ""


# Initialize the tool
get_file_path_tool = Tool(
    name="file_path_retriever",
    func=get_file_path,
    description="Retrieves path to the reference file."
)

get_content_tool = Tool(
    name="ref_content_retriever",
    func=get_ref_content,
    description="Retrieves reference file content."
)

search_web_tool = Tool(
    name="search_web_retriever",
    func=search_web,
    description="Retrieves online info about a specific topic."
)

extract_text_tool = Tool(
    name="extract_text_retriever",
    func=extract_text_from_image,
    description="Retrieves text from an image."
)


chat = ChatOllama(model=model_id, verbose=True)
print(f"Model {chat.model} downloaded!")

tools = [get_file_path_tool, get_content_tool, extract_text_tool, search_web_tool]
chat_with_tools = chat.bind_tools(tools, parallel_tool_calls=False)


# Generate the AgentState and Agent graph
class AgentState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]


def assistant(state: AgentState):
    return {
        "messages": chat.invoke(state["messages"]),
    }


# The graph
builder = StateGraph(AgentState)

# Define nodes: these do the work
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode([get_file_path_tool, get_content_tool, extract_text_tool, search_web_tool]))

# Define edges: these determine how the control flow moves
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
    "assistant",
    # If the latest message requires a tool, route to tools
    # Otherwise, provide a direct response
    tools_condition
)
builder.add_edge("tools", "assistant")
alfred = builder.compile()
system_prompt = SystemMessage(
    content="You are a general AI assistant. \
        I will ask you a question. Report your thoughts shortly, \
        and finish your answer with the following template: \
        FINAL ANSWER: [YOUR FINAL ANSWER]. \
        YOUR FINAL ANSWER should be a number OR as few words as possible \
        OR a comma separated list of numbers and/or strings. \
        If you are asked for a number, use only digits in your final answer. \
        Don't use comma nor brackets to write your number neither use units such as $ or percent sign \
        unless specified otherwise. \
        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), \
        and write the digits in plain text unless specified otherwise. \
        If you are asked for a comma separated list, apply the above rules \
        depending of whether the element to be put in the list is a number or a string. \
        If the question refers to an external content and there is no reference file attached, \
        perform a web search and retrieve relevant information from the internet. \
        Make sure that each final answer is preceded with 'FINAL ANSWER:'. "
)


class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        message = HumanMessage(content=question)
        answer = alfred.invoke(input={"messages": [system_prompt, message]},config={"recursion_limit": 3})['messages'][-1].content
        answer = "".join(re.findall(r'(FINAL ANSWER:.*)', answer, flags=re.M))    
        answer = answer.replace('FINAL ANSWER: ', '')
        answer = answer.replace('[', '')
        fixed_answer = answer.replace(']', '')
        print(f"Agent returning fixed answer: {fixed_answer}")
        return fixed_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 ( useful 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 separate 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)