app.py

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

# Import smol-agent and tool components
from smolagents import CodeAgent, LiteLLMModel, tool
from smolagents import DuckDuckGoSearchTool
from unstructured.partition.auto import partition

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Tool Definition ---
@tool
def file_reader(file_path: str) -> str:
    """Reads the content of a file and returns its text content.

    This tool supports various file types like PDF, TXT, CSV, etc., from either
    a local path or a web URL.

    Args:
        file_path (str): The local path or web URL of the file to be read.
    """
    try:
        if file_path.startswith("http://") or file_path.startswith("https://"):
            response = requests.get(file_path, timeout=20)
            response.raise_for_status()
            with open("temp_file", "wb") as f:
                f.write(response.content)
            elements = partition("temp_file")
            os.remove("temp_file") # Clean up
        else:
            elements = partition(file_path)
        return "\n\n".join([str(el) for el in elements])
    except Exception as e:
        return f"Error reading or processing file '{file_path}': {e}"

# --- Agent Class ---
class GaiaSmolAgent:
    def __init__(self):
        """
        Initializes the optimized agent.
        Optimization 1: Use a faster LLM (Gemini 1.5 Flash) to reduce latency.
        Optimization 2: Use a single, powerful agent with a detailed system prompt
                        to eliminate the slow two-step (plan -> execute) process.
        """
        print("Initializing Optimized GaiaSmolAgent...")
        api_key = os.getenv("GEMINI_API_KEY")
        if not api_key:
            raise ValueError("API key 'GEMINI_API_KEY' not found in environment secrets.")

        # Use a faster, more cost-effective model optimized for speed.
        model = LiteLLMModel(
            model_id="gemini/gemini-1.5-flash-latest",
            api_key=api_key,
            temperature=0.0,
            timeout=120.0, # Add a timeout to prevent hanging
        )

        # Store the sophisticated system prompt as an instance variable.
        self.system_prompt = """
        You are an expert-level research assistant AI. Your sole purpose is to answer the user's question by breaking it down into logical steps and using the provided tools.

        **Available Tools:**
        - `duck_duck_go_search(query: str) -> str`: Use this to find information, file URLs, or anything on the web.
        - `file_reader(file_path: str) -> str`: Use this to read the contents of a file from a local path or a web URL.

        **Your Thought Process:**
        1.  **Deconstruct the Goal:** Carefully analyze the question to understand what information is needed.
        2.  **Formulate a Plan:** Think step-by-step about which tools to use in what order. For example, you might need to search for a URL first, then read the content of that URL.
        3.  **Execute & Analyze:** Call the necessary tools. Carefully examine the output of each tool to extract the required facts. You can write Python code to process the data returned by the tools.
        4.  **Synthesize the Answer:** Once you have gathered sufficient information, formulate a final, concise answer to the original question.

        **CRITICAL INSTRUCTIONS:**
        - Your final action MUST be a single call to the `final_answer(answer: str)` function.
        - The `answer` argument must be a string containing only the definitive answer.
        - All code you write is executed in a restricted Python environment. You can define variables and write logic to process the tool outputs before calling `final_answer`.
        - Do not ask for clarification. Directly proceed to solve the problem.
        """

        # Initialize the agent without the 'system_prompt' argument to prevent the TypeError.
        self.agent = CodeAgent(
            model=model,
            tools=[file_reader, DuckDuckGoSearchTool()],
            add_base_tools=True,  # Provides the python interpreter and the final_answer function
        )
        print("Optimized GaiaSmolAgent initialized successfully.")

    def __call__(self, question: str) -> str:
        """
        Directly runs the agent to generate and execute a plan to answer the question.
        This simplified single-call approach is faster and more efficient.
        """
        print(f"Optimized Agent received question: {question[:100]}...")
        try:
            # Combine the system prompt with the actual question to give the agent full context.
            full_prompt = f"{self.system_prompt}\n\nUser Question: \"{question}\""
            # The agent now internally handles the reasoning, code generation, and execution in one step.
            final_answer = self.agent.run(full_prompt)
        except Exception as e:
            print(f"FATAL AGENT ERROR: An exception occurred during agent execution: {e}")
            print(traceback.format_exc()) # Print full traceback for easier debugging
            return f"FATAL AGENT ERROR: {e}"

        print(f"Optimized Agent returning final answer: {final_answer}")
        return str(final_answer)

# --- Main Application Logic (Unchanged) ---
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 = GaiaSmolAgent()
    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


# --- Gradio Interface (Updated Instructions) ---
with gr.Blocks() as demo:
    gr.Markdown("# GAIA Agent Evaluation Runner (smol-agent)")
    gr.Markdown(
        """
        **Instructions:**
        1.  Ensure you have added your **GEMINI API key** (as `GEMINI_API_KEY`) in the Space's secrets.
        2.  Log in to your Hugging Face account using the button below.
        3.  Click 'Run Evaluation & Submit All Answers' to run your agent and see the score.
        """
    )
    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    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("Launching Gradio Interface for GAIA Agent Evaluation...")
    demo.launch(debug=True, share=False)