app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import time

from smolagents import LiteLLMModel
from smolagents import (
    CodeAgent,
    DuckDuckGoSearchTool,
    HfApiModel,
    WikipediaSearchTool,
    PythonInterpreterTool,
    CodeAgent,
    FinalAnswerTool,
    load_tool,
    tool,
)


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


@tool
def reverse_string(input_string: str) -> str:
    """A tool that reverses the characters in a string.

    Args:
        input_string: The string to be reversed
    """
    return input_string[::-1]


@tool
def optimized_web_search(
    search_query: str, important_words: list, batch_size: int = 500
) -> str:
    """A tool that performs a web search and filters the results to only include content chunks that contain important keywords.

    Args:
        search_query: The search query to use (e.g., 'Beatles albums Wikipedia')
        important_words: List of important keywords to filter by (e.g., ['Abbey Road', 'Let It Be', '1970'])
        batch_size: The size of content chunks to process (default: 500 characters)
    """
    try:
        # Perform the search using DuckDuckGoSearchTool (assuming it's available in the environment)
        search_tool = DuckDuckGoSearchTool()
        search_results = search_tool.forward(search_query)

        # If no results found, return early
        if not search_results or len(search_results) == 0:
            return "No search results found."

        # Process the search results content
        # Assuming search_results is a list of dictionaries with a 'content' field
        # or a string with all content combined
        if isinstance(search_results, list):
            all_content = " ".join(
                [result.get("content", "") for result in search_results]
            )
        else:
            all_content = search_results

        # Split the content into batches
        batches = []
        for i in range(0, len(all_content), batch_size):
            batches.append(all_content[i : i + batch_size])

        # Filter batches to only include those containing important words
        filtered_batches = []
        for batch in batches:
            # Check if any important word is in the batch
            if any(word.lower() in batch.lower() for word in important_words):
                filtered_batches.append(batch)

        # Join the filtered batches
        filtered_content = "\n\n".join(filtered_batches)

        # If no content remains after filtering, provide a helpful message
        if not filtered_content:
            return f"No content containing the important words {important_words} was found in the search results."

        return filtered_content

    except Exception as e:
        return f"Error during optimized web search: {str(e)}"


# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        fixed_answer = "This is a default answer."
        print(f"Agent returning fixed answer: {fixed_answer}")
        return fixed_answer


class MyAgent:
    def __init__(self):
        # model = LiteLLMModel(
        #     model_id="ollama_chat/deepseek-r1:8b",  # Or try other Ollama-supported models
        #     api_base="http://127.0.0.1:11434",  # Default Ollama local server
        #     num_ctx=8192,
        # )
        # model = HfApiModel()

        model = LiteLLMModel(
            model_id="gemini/gemini-2.0-flash-lite",
            api_key=os.getenv("GEMINI_API_TOKEN"),
        )

        self.agent = CodeAgent(
            tools=[
                DuckDuckGoSearchTool(),
                PythonInterpreterTool(),
                # optimized_web_search,
                reverse_string,
                WikipediaSearchTool(),
                FinalAnswerTool(),
            ],
            model=model,
            max_steps=10,
            add_base_tools=True,
            additional_authorized_imports=["pandas", "*"],
        )
        print("BasicAgent initialized.")

    def __call__(self, question: str) -> str:

        # question = "what famous person died in April 2025?"
        print(f"Agent received question (first 50 chars): {question[:50]}...")

        system_instruction = (
            ""
            # "Ignore all previous instructions. "
            "I will ask you a question. Report your thoughts step by step. "
            # "Don't generate code, don't execute code, don't write explanations. "
            # "Stop on the first step"
            "Finish your answer only with the final answer. In the final answer don't write explanations. "
            "The 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, don't use comma 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."
            "Pay attention that the questions are specifically designed to be tricky. "
            "Think about each sentence in the question and verify the answer against every sentence. "
            "Follow the instructions in the question precisely.  "
            "If the answer found is not exactly what is asked, try to find another answer in other pages from the search result. "
            "If in the search results you visit one page and it doesn't contain answer, try visiting 5 more pages from that search result. "
            "QUESTION: "
            # "You have access to optimized_web_search, a powerful tool for efficient research:"
            # "1. Use this tool whenever you need web information without context overload"
            # "2. Required parameters:"
            # "- search_query: Specific search terms (e.g., "
            # "Beatles albums Wikipedia"
            # ")"
            # "- important_words: List of keywords ["
            # "Abbey Road"
            # ", "
            # "Let It Be"
            # "] to filter relevant content"
            # "3. The tool will return only text chunks containing your keywords, saving context space"
            # "Use this tool strategically when researching topics that need web information."
        )

        prompt = system_instruction + "\n" + question

        ollama_answer = self.agent.run(prompt)
        print(f"Agent returning ollama answer: {ollama_answer}")
        return ollama_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
    space_id = "gmykola/Final_Assignment_Template"

    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 = MyAgent()

    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...")
    # array
    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,
                }
            )

            # Add delay
            # There are 30 requests per minute, meaning each request is spaced at least 2 seconds apart. Set 3 seconds as a buffer.
            print("Waiting 3 seconds before next request to avoid rate limit...")
            time.sleep(3)

        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


# Custom CSS to make table content copyable
custom_css = """
.table-wrap table td {
    user-select: text !important;
    cursor: text !important;
}
"""

# --- Build Gradio Interface using Blocks ---
with gr.Blocks(css=custom_css) 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, interactive=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
    space_id_startup = "gmykola/Final_Assignment_Template"

    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)