import os
import gradio as gr
import requests
import pandas as pd
import subprocess # Needed for runtime pip install
import sys        # Needed for runtime pip install

# --- START: Force ddgs installation workaround ---
# This block ensures 'ddgs' (which provides 'duckduckgo_search') is installed
# early, before smolagents tries to use its DuckDuckGoSearchTool.
try:
    # Attempt to import duckduckgo_search to check if it's already available
    import duckduckgo_search
    print("duckduckgo_search (via ddgs) is already installed.")
except ImportError:
    print("duckduckgo_search not found. Attempting to install ddgs...")
    try:
        # Use 'ddgs' as it's the updated package name
        subprocess.check_call([sys.executable, "-m", "pip", "install", "ddgs>=4.0.0"])
        print("ddgs installed successfully.")
    except Exception as e:
        print(f"Failed to install ddgs: {e}")
        # Critical error: if ddgs can't be installed, the app can't function.
        raise RuntimeError(f"CRITICAL: Failed to install ddgs: {e}")
# --- END: Force ddgs installation workaround ---

# Now import the agent, as its dependencies (smolagents, duckduckgo_search) should be ready
from agent import GaiaAgent

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

def run_agent_and_score(task_description: str) -> str:
    # Initialize the agent within the function, so it's fresh for each run
    # This also helps if the agent initialization is heavy or stateful
    gaia_agent = GaiaAgent()
    
    # Process the task
    agent_output = gaia_agent.process_task(task_description)
    
    # Send output to the scoring API
    try:
        response = requests.post(
            f"{DEFAULT_API_URL}/score_agent",
            json={"task_description": task_description, "agent_response": agent_output}
        )
        response.raise_for_status()  # Raise an HTTPError for bad responses (4xx or 5xx)
        scoring_result = response.json()
        score_info = f"Scoring Result:\nTotal Score: {scoring_result.get('total_score')}\nCorrectness Score: {scoring_result.get('correctness_score')}\nExplanation: {scoring_result.get('explanation', 'No explanation provided.')}"
        return f"Agent Output:\n{agent_output}\n\n---\n\n{score_info}"
    except requests.exceptions.RequestException as e:
        return f"Agent Output:\n{agent_output}\n\n---\n\nError connecting to scoring API: {e}"
    except Exception as e:
        return f"Agent Output:\n{agent_output}\n\n---\n\nAn unexpected error occurred during scoring: {e}"

# Gradio Interface setup
with gr.Blocks() as demo:
    gr.Markdown("# GAIA Basic Agent Evaluator (Freddolin)")
    gr.Markdown("Enter a task description for the agent to process. The agent's output will be displayed, followed by its score from the GAIA scoring API.")

    task_input = gr.Textbox(label="Task Description", placeholder="e.g., 'What is the capital of France?'")
    output_text = gr.Textbox(label="Agent Output & Score", interactive=False)

    run_button = gr.Button("Run Agent & Score")

    run_button.click(
        fn=run_agent_and_score,
        inputs=task_input,
        outputs=output_text
    )

# Launch the Gradio app
demo.launch(debug=True) # debug=True can provide more info in logs during development