app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from huggingface_hub import InferenceClient  # Import Hugging Face InferenceClient

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

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------


class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        # Use Qwen2.5-7B-Instruct model
        self.model_name = "Qwen/Qwen2.5-7B-Instruct"
        self.hf_token = os.getenv("HF_TOKEN")  # Get token from environment if available

        try:
            print(f"Initializing model: {self.model_name}")
            self.hf_client = InferenceClient(
                model=self.model_name,
                token=self.hf_token
            )
            print(f"Model initialized successfully: {self.model_name}")
        except Exception as e:
            print(f"Error initializing model ({self.model_name}): {e}")
            self.hf_client = None
            print("WARNING: Model initialization failed. Agent may not function properly.")

    def break_down_question(self, question: str) -> list:
        """
        Use an LLM to break down a complex question into key search terms or sub-questions.

        Args:
            question (str): The original question

        Returns:
            list: A list of key search terms or sub-questions
        """
        try:
            print(f"Breaking down question with LLM: {question[:50]}...")

            # Create a prompt that asks the LLM to break down the question
            prompt = f"""
            Please break down this question into 2-3 key search queries that would help find information to answer it.
            Return ONLY the search queries, one per line, with no additional text or explanations.

            Question: {question}
            """

            # Call the Hugging Face model to get the breakdown
            response = self.hf_client.text_generation(
                prompt=prompt,
                max_new_tokens=150,
                temperature=0.3,
                repetition_penalty=1.1,
                do_sample=True
            )

            # Extract the search terms from the response
            search_terms = response.strip().split('\n')
            search_terms = [term.strip() for term in search_terms if term.strip()]

            # Limit to 3 search terms maximum
            search_terms = search_terms[:3]

            print(f"Question broken down into {len(search_terms)} search terms: {search_terms}")
            return search_terms

        except Exception as e:
            print(f"Error breaking down question: {e}")
            # If there's an error, return the original question as a fallback
            return [question]

    def search_internet(self, query: str) -> str:
        """
        Search the internet for information using Wikipedia's API.
        This is a simple implementation that returns search results as text.

        Args:
            query (str): The search query

        Returns:
            str: Search results as text
        """
        print(f"Searching internet for: {query}")
        try:
            # Use Wikipedia API to search for information
            headers = {
                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
            }

            # Step 1: Search for relevant articles
            search_url = f"https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch={query}&format=json"
            search_response = requests.get(search_url, headers=headers, timeout=10)
            search_response.raise_for_status()
            search_data = search_response.json()

            # Check if we found any search results
            if 'query' not in search_data or 'search' not in search_data['query'] or not search_data['query']['search']:
                return "No relevant information found."

            # Get the title of the first (most relevant) result
            first_result = search_data['query']['search'][0]
            page_title = first_result['title']

            # Step 2: Fetch the content of the most relevant article
            content_url = f"https://en.wikipedia.org/w/api.php?action=query&prop=extracts&exintro=1&explaintext=1&titles={page_title}&format=json"
            content_response = requests.get(content_url, headers=headers, timeout=10)
            content_response.raise_for_status()
            content_data = content_response.json()

            # Extract the page content
            pages = content_data['query']['pages']
            page_id = list(pages.keys())[0]

            if 'extract' in pages[page_id]:
                extract = pages[page_id]['extract']
                # Limit extract length to avoid very long responses
                if len(extract) > 1000:
                    extract = extract[:1000] + "..."

                result = f"Wikipedia article: {page_title}\n\n{extract}"

                # Also get a few more related article titles
                related_titles = []
                for item in search_data['query']['search'][1:4]:  # Get next 3 results
                    related_titles.append(item['title'])

                if related_titles:
                    result += "\n\nRelated topics:\n"
                    for title in related_titles:
                        result += f"- {title}\n"

                return result
            else:
                return "Found a relevant page, but couldn't extract its content."

        except Exception as e:
            print(f"Error searching internet: {e}")
            return f"Error performing internet search: {str(e)}"

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")

        # Use LLM to break down the question into key search terms
        search_terms = self.break_down_question(question)

        # Search for information using each search term
        all_results = []
        for term in search_terms:
            result = self.search_internet(term)
            if result and result != "No relevant information found." and not result.startswith("Error"):
                all_results.append(result)

        # Create a response based on collected search results
        if all_results:
            # Join the results with clear separation
            combined_results = "\n\n--- Next Search Result ---\n\n".join(all_results)

            # Use Hugging Face model to synthesize a coherent answer from the search results
            try:
                synthesis_prompt = f"""
                Based on the following search results, please provide a comprehensive answer to this question:

                Question: {question}

                Search Results:
                {combined_results}

                Answer:
                """

                # Call the Hugging Face model to synthesize an answer
                response = self.hf_client.text_generation(
                    prompt=synthesis_prompt,
                    max_new_tokens=500,
                    temperature=0.5,
                    repetition_penalty=1.05,
                    do_sample=True
                )

                answer = response.strip()
                print("Agent returning synthesized answer from search results.")
                return answer

            except Exception as e:
                print(f"Error synthesizing answer: {e}")
                # Fallback to returning the raw search results
                answer = f"Based on my searches, I found this information:\n\n{combined_results}"
                print("Agent returning raw search results due to synthesis error.")
                return answer
        else:
            # Fallback to default answer if all searches fail
            answer = "I couldn't find specific information about that question."
            print("Agent returning default answer as searches found no useful information.")
            return 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)

    return

    # 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}"})

        return

    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 (Attempt #3)")
    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)