app.py

from smolagents import CodeAgent, HfApiModel, OpenAIServerModel

from Gradio_UI import GradioUI
import yaml
import os
import requests
import pandas as pd
import gradio as gr
import time

# Import tool CLASSES from the src directory
from src.final_answer_tool import FinalAnswerTool
from src.web_browsing_tool import WebBrowser
from src.file_processing_tool import FileIdentifier
from src.image_processing_tool import ImageProcessor
from src.markdown_table_parser import MarkdownTableParserTool # Updated
from src.python_tool import CodeExecutionTool
from src.speech_to_text import SpeechToTextTool # Updated
from src.spreadsheet_tool import SpreadsheetTool
from src.text_reversal_tool import TextReversalTool
from src.video_processing_tool import VideoProcessingTool

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

# --- Basic Agent Definition ---

# # Configure the Language Model. Overloaded or not available
# model = HfApiModel(
#     max_tokens=2096,
#     temperature=0.5,
# #    model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud/', # nope
# #    model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
#     custom_role_conversions=None,
# )

# Try to use OpenAI API
model = OpenAIServerModel(
    model_id="gpt-4o",
    api_base="https://api.openai.com/v1",
    api_key=os.environ["OPENAI_API_KEY"],
)

# Instantiate Tools
final_answer_tool = FinalAnswerTool()
web_browsing_tool = WebBrowser() 
file_processing_tool = FileIdentifier() 
image_processing_tool = ImageProcessor() 
markdown_parser_tool = MarkdownTableParserTool() # Updated
python_tool = CodeExecutionTool() 
speech_to_text_tool = SpeechToTextTool() # Updated
spreadsheet_tool = SpreadsheetTool() 
text_reversal_tool = TextReversalTool()
video_processing_tool = VideoProcessingTool() 

# Load Prompts
try:
    with open("prompts.yaml", 'r') as stream:
        prompt_templates = yaml.safe_load(stream)
except FileNotFoundError:
    print("Error: prompts.yaml not found. Please ensure it is in the root project directory.")
    prompt_templates = {} # Fallback to empty templates
except yaml.YAMLError as e:
    print(f"Error parsing prompts.yaml: {e}")
    prompt_templates = {}


# Enhance the agent class to conform to the template's interface
class EnhancedCodeAgent(CodeAgent):
    def __call__(self, question: str) -> str:
        response = self.run(question)
        return response

# Create the Agent
agent_tools = [
    final_answer_tool,
    web_browsing_tool,
    file_processing_tool,
    image_processing_tool,
    markdown_parser_tool, # Updated
    python_tool,
    speech_to_text_tool, # Updated
    spreadsheet_tool,
    text_reversal_tool,
    video_processing_tool
]

agent = EnhancedCodeAgent(
    model=model,
    tools=agent_tools,
    max_steps=15,
    verbosity_level=1, 
    name="ComprehensiveQuestionAgent",
    description="An agent equipped with a suite of tools to answer diverse questions from the common_questions.json set.",
    prompt_templates=prompt_templates
)

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the agent on them, submits answers,
    and displays the results.
    """
    space_id = os.getenv("SPACE_ID")

    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. Use existing agent
    try:
        # agent is already instantiated globally
        if not agent:
            return "Error: Agent not initialized", None
    except Exception as e:
        print(f"Error accessing agent: {e}")
        return f"Error accessing agent: {e}", None

    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

# Launch the Gradio UI
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")

    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(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")

    # Build Gradio Interface using Blocks
    with gr.Blocks() as demo:
        gr.Markdown("# Enhanced 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).
            """
        )

        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]
        )

    print("Launching Gradio Interface...")
    demo.launch(debug=True, share=False)