app.py

import os
import gradio as gr
import requests
import pandas as pd
import traceback
import time 
import mimetypes
from tempfile import NamedTemporaryFile

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

# Imports for advanced file processing
import speech_recognition as sr
from pydub import AudioSegment

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

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

# --- Tool Definition (Upgraded for Full Multimodality with pydub) ---
@tool
def file_reader(file_path: str) -> str:
    """
    Reads and analyzes the content of a file and returns relevant text-based information.
    Supports:
      - Text files (PDF, TXT, CSV)
      - Images (PNG, JPG) with OCR
      - Audio (MP3, WAV) via speech recognition
      - Video (MP4, MOV) via speech recognition on audio track
    Can be used with a local file path or a web URL.

    Args:
        file_path (str): The local path or web URL of the file to be read.
    Returns:
        str: Extracted or transcribed content as text.
    """
    temp_file_path = None
    audio_temp_path = None
    try:
        # Download the file if it's a URL
        if file_path.startswith("http://") or file_path.startswith("https://"):
            temp_file_path = NamedTemporaryFile(delete=False).name
            response = requests.get(file_path, timeout=20)
            response.raise_for_status()
            with open(temp_file_path, "wb") as f:
                f.write(response.content)
            local_path = temp_file_path
        else:
            local_path = file_path

        mime_type, _ = mimetypes.guess_type(local_path)
        recognizer = sr.Recognizer()

        if mime_type:
            # Handle audio files
            if mime_type.startswith("audio/"):
                with sr.AudioFile(local_path) as source:
                    audio = recognizer.record(source)
                    return recognizer.recognize_whisper(audio)

            # Handle video files by extracting audio with pydub
            elif mime_type.startswith("video/"):
                with NamedTemporaryFile(suffix=".wav", delete=False) as audio_temp:
                    audio_temp_path = audio_temp.name
                
                # Extract audio using pydub
                video_audio = AudioSegment.from_file(local_path, format=mime_type.split('/')[1])
                video_audio.export(audio_temp_path, format="wav")
                
                with sr.AudioFile(audio_temp_path) as source:
                    audio = recognizer.record(source)
                
                return recognizer.recognize_whisper(audio)

        # Default to handling text and images with OCR if not audio/video
        elements = partition(local_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}"
    finally:
        # Clean up the downloaded file if it exists
        if temp_file_path and os.path.exists(temp_file_path):
            os.remove(temp_file_path)
        # Clean up the temporary audio file
        if audio_temp_path and os.path.exists(audio_temp_path):
            os.remove(audio_temp_path)



# --- Agent Class (Updated with More Powerful Model and Tools) ---
class GaiaSmolAgent:
    def __init__(self):
        """
        Initializes the optimized agent.
        Now uses a more powerful model and the agent's native conversation memory.
        """
        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 more powerful, "clever" model for better reasoning.
        model = LiteLLMModel(
            model_id="gemini/gemini-1.5-pro-latest",
            api_key=api_key,
            temperature=0.0,
            timeout=120.0, # Add a timeout to prevent hanging
        )

        # --- CHANGE 1: ENHANCED SYSTEM PROMPT ---
        # A more detailed prompt that guides the agent on how to handle GAIA-specific challenges,
        # such as precise data extraction, calculations, and structured reasoning.
        self.system_prompt = """
        You are an expert-level research assistant AI, specifically designed to solve challenging questions from the GAIA benchmark. Your goal is to provide a precise and accurate final answer by meticulously following a step-by-step plan.

        **Available Tools:**
        - `duck_duck_go_search(query: str) -> str`: Use this for web searches to find information, URLs, facts, etc.
        - `file_reader(file_path: str) -> str`: Use this to read content from local files or web URLs. It handles text, PDFs, images (OCR), audio, and video.

        **Your Thought Process & Execution Strategy:**
        1.  **Analyze the Question:** First, break down the user's question to fully understand all its components, constraints, and the exact type of information required for the answer (e.g., a number, a date, a name).
        2.  **Formulate a Step-by-Step Plan:** Before using any tools, you MUST outline your plan in your thoughts. For example: "Step 1: Search for the document URL. Step 2: Use the file_reader to read the document. Step 3: Extract the specific data point. Step 4: Perform calculation if needed. Step 5: Provide the final answer."
        3.  **Execute and Verify:** Execute your plan one step at a time. After each tool call, review the output. Verify if the information obtained is sufficient and accurate. If a step fails or the result is not what you expected, REVISE your plan.
        4.  **Synthesize the Answer:** Once you have gathered and verified all necessary information, formulate the final answer. Use the Python interpreter for any calculations, data sorting, or text processing to ensure accuracy.

        **CRITICAL INSTRUCTIONS:**
        - **Precision is Key:** Pay close attention to the requested format of the final answer. If a question asks for a number, your final answer must be only that number.
        - **Code for Calculations:** ALWAYS use the Python interpreter for any calculations, date comparisons, or data manipulation. Do not perform calculations in your head.
        - **Autonomous Operation:** You must work autonomously. Make the most logical deduction based on the information you gather. Do not ask for clarification.
        - **Final Answer:** Your final output MUST be a single call to the `final_answer(answer: str)` function with the precise answer.
        """

        # Initialize the agent with the updated file_reader tool and memory settings.
        self.agent = CodeAgent(
            model=model,
            tools=[file_reader, DuckDuckGoSearchTool()],
            add_base_tools=True,  # Provides python interpreter and final_answer
            
            # --- CHANGE 2: MORE REACTIVE PLANNING ---
            # By setting planning_interval=1, the agent re-evaluates its plan
            # after every single tool execution. This allows it to immediately course-correct
            # based on new information, which is vital for complex, multi-step tasks.
            planning_interval=1
        )
        
        print("Optimized GaiaSmolAgent initialized successfully with enhanced prompt and reactive planning.")

    def __call__(self, question: str, reset_memory: bool = False) -> str:
        """
        Directly runs the agent to generate and execute a plan to answer the question.
        It leverages the agent's built-in memory, controlled by the `reset` parameter.

        Args:
            question (str): The user's question.
            reset_memory (bool): If True, the agent's conversation memory will be cleared
                                 before running. Maps to the agent's `reset` parameter.
        """
        print(f"Optimized Agent received question: {question[:100]}...")
        
        try:
            # Combine the system prompt with the current question. The agent will handle the history.
            full_prompt = f"{self.system_prompt}\n\nCURRENT TASK:\nUser Question: \"{question}\""
            
            # Use the agent's `reset` parameter to control conversation memory.
            # `reset=False` keeps the memory from previous calls.
            final_answer = self.agent.run(full_prompt, reset=reset_memory)
        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)