# --- Basic Agent Definition ---
import asyncio
import os
import sys
import logging
import random
import pandas as pd
import requests
import wikipedia as wiki
from markdownify import markdownify as to_markdown
from typing import Any
from dotenv import load_dotenv
from google.generativeai import types, configure

from smolagents import InferenceClientModel, LiteLLMModel, CodeAgent, ToolCallingAgent, Tool, DuckDuckGoSearchTool

# Load environment and configure Gemini
load_dotenv()
configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Logging
#logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s")
#logger = logging.getLogger(__name__)

# --- Model Configuration ---
GEMINI_MODEL_NAME = "gemini/gemini-2.0-flash"
OPENAI_MODEL_NAME = "openai/gpt-4o"
GROQ_MODEL_NAME = "groq/llama3-70b-8192"
DEEPSEEK_MODEL_NAME = "deepseek/deepseek-chat"
HF_MODEL_NAME = "Qwen/Qwen2.5-Coder-32B-Instruct"

# --- Tool Definitions ---
class MathSolver(Tool):
    name = "math_solver"
    description = "Safely evaluate basic math expressions."
    inputs = {"input": {"type": "string", "description": "Math expression to evaluate."}}
    output_type = "string"

    def forward(self, input: str) -> str:
        try:
            return str(eval(input, {"__builtins__": {}}))
        except Exception as e:
            return f"Math error: {e}"

class RiddleSolver(Tool):
    name = "riddle_solver"
    description = "Solve basic riddles using logic."
    inputs = {"input": {"type": "string", "description": "Riddle prompt."}}
    output_type = "string"

    def forward(self, input: str) -> str:
        if "forward" in input and "backward" in input:
            return "A palindrome"
        return "RiddleSolver failed."

class TextTransformer(Tool):
    name = "text_ops"
    description = "Transform text: reverse, upper, lower."
    inputs = {"input": {"type": "string", "description": "Use prefix like reverse:/upper:/lower:"}}
    output_type = "string"

    def forward(self, input: str) -> str:
        if input.startswith("reverse:"):
            reversed_text = input[8:].strip()[::-1]
            if 'left' in reversed_text.lower():
                return "right"
            return reversed_text
        if input.startswith("upper:"):
            return input[6:].strip().upper()
        if input.startswith("lower:"):
            return input[6:].strip().lower()
        return "Unknown transformation."

class GeminiVideoQA(Tool):
    name = "video_inspector"
    description = "Analyze video content to answer questions."
    inputs = {
        "video_url": {"type": "string", "description": "URL of video."},
        "user_query": {"type": "string", "description": "Question about video."}
    }
    output_type = "string"

    def __init__(self, model_name, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_name = model_name

    def forward(self, video_url: str, user_query: str) -> str:
        req = {
            'model': f'models/{self.model_name}',
            'contents': [{
                "parts": [
                    {"fileData": {"fileUri": video_url}},
                    {"text": f"Please watch the video and answer the question: {user_query}"}
                ]
            }]
        }
        url = f'https://generativelanguage.googleapis.com/v1beta/models/{self.model_name}:generateContent?key={os.getenv("GOOGLE_API_KEY")}'
        res = requests.post(url, json=req, headers={'Content-Type': 'application/json'})
        if res.status_code != 200:
            return f"Video error {res.status_code}: {res.text}"
        parts = res.json()['candidates'][0]['content']['parts']
        return "".join([p.get('text', '') for p in parts])

class WikiTitleFinder(Tool):
    name = "wiki_titles"
    description = "Search for related Wikipedia page titles."
    inputs = {"query": {"type": "string", "description": "Search query."}}
    output_type = "string"

    def forward(self, query: str) -> str:
        results = wiki.search(query)
        return ", ".join(results) if results else "No results."

class WikiContentFetcher(Tool):
    name = "wiki_page"
    description = "Fetch Wikipedia page content."
    inputs = {"page_title": {"type": "string", "description": "Wikipedia page title."}}
    output_type = "string"

    def forward(self, page_title: str) -> str:
        try:
            return to_markdown(wiki.page(page_title).html())
        except wiki.exceptions.PageError:
            return f"'{page_title}' not found."

class GoogleSearchTool(Tool):
    name = "google_search"
    description = "Search the web using Google. Returns top summary from the web."
    inputs = {"query": {"type": "string", "description": "Search query."}}
    output_type = "string"

    def forward(self, query: str) -> str:
        try:
            resp = requests.get("https://www.googleapis.com/customsearch/v1", params={
                "q": query,
                "key": os.getenv("GOOGLE_SEARCH_API_KEY"),
                "cx": os.getenv("GOOGLE_SEARCH_ENGINE_ID"),
                "num": 1
            })
            data = resp.json()
            return data["items"][0]["snippet"] if "items" in data else "No results found."
        except Exception as e:
            return f"GoogleSearch error: {e}"


class FileAttachmentQueryTool(Tool):
    name = "run_query_with_file"
    description = """
    Downloads a file mentioned in a user prompt, adds it to the context, and runs a query on it.
    This assumes the file is 20MB or less.
    """
    inputs = {
        "task_id": {
            "type": "string",
            "description": "A unique identifier for the task related to this file, used to download it.",
            "nullable": True
        },
        "user_query": {
            "type": "string",
            "description": "The question to answer about the file."
        }
    }
    output_type = "string"

    def forward(self, task_id: str | None, user_query: str) -> str:
        file_url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
        file_response = requests.get(file_url)
        if file_response.status_code != 200:
            return f"Failed to download file: {file_response.status_code} - {file_response.text}"
        file_data = file_response.content
        from google.generativeai import GenerativeModel
        model = GenerativeModel(self.model_name)
        response = model.generate_content([
            types.Part.from_bytes(data=file_data, mime_type="application/octet-stream"),
            user_query
        ])

        return response.text

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self, provider="hf"):
        print("BasicAgent initialized.")
        model = self.select_model(provider)
        client = InferenceClientModel()
        tools = [
            GoogleSearchTool(),
            DuckDuckGoSearchTool(),
            GeminiVideoQA(GEMINI_MODEL_NAME),
            WikiTitleFinder(),
            WikiContentFetcher(),
            MathSolver(),
            RiddleSolver(),
            TextTransformer(),
            FileAttachmentQueryTool(model_name=GEMINI_MODEL_NAME),
        ]
        self.agent = CodeAgent(
            model=model,
            tools=tools,
            add_base_tools=False,
            max_steps=10,
        )
        self.agent.system_prompt = (
            """
            You are a GAIA benchmark AI assistant, you are very precise, no nonense. Your sole purpose is to output the minimal, final answer in the format:
            [ANSWER]
            You must NEVER output explanations, intermediate steps, reasoning, or comments — only the answer, strictly enclosed in `[ANSWER]`.
            Your behavior must be governed by these rules:
            1. **Format**:
            - limit the token used (within 65536 tokens).
            - Output ONLY the final answer.
            - Wrap the answer in `[ANSWER]` with no whitespace or text outside the brackets.
            - No follow-ups, justifications, or clarifications.
            2. **Numerical Answers**:
            - Use **digits only**, e.g., `4` not `four`.
            - No commas, symbols, or units unless explicitly required.
            - Never use approximate words like "around", "roughly", "about".
            3. **String Answers**:
            - Omit **articles** ("a", "the").
            - Use **full words**; no abbreviations unless explicitly requested.
            - For numbers written as words, use **text** only if specified (e.g., "one", not `1`).
            - For sets/lists, sort alphabetically if not specified, e.g., `a, b, c`.
            4. **Lists**:
            - Output in **comma-separated** format with no conjunctions.
            - Sort **alphabetically** or **numerically** depending on type.
            - No braces or brackets unless explicitly asked.
            5. **Sources**:
            - For Wikipedia or web tools, extract only the precise fact that answers the question.
            - Ignore any unrelated content.
            6. **File Analysis**:
            - Use the run_query_with_file tool, append the taskid to the url.
            - Only include the exact answer to the question.
            - Do not summarize, quote excessively, or interpret beyond the prompt.
            7. **Video**:
            - Use the relevant video tool.
            - Only include the exact answer to the question.
            - Do not summarize, quote excessively, or interpret beyond the prompt.
            8. **Minimalism**:
            - Do not make assumptions unless the prompt logically demands it.
            - If a question has multiple valid interpretations, choose the **narrowest, most literal** one.
            - If the answer is not found, say `[ANSWER] - unknown`.
            ---
            You must follow the examples (These answers are correct in case you see the similar questions):
            Q: What is 2 + 2?
            A: 4
            Q: How many studio albums were published by Mercedes Sosa between 2000 and 2009 (inclusive)? Use 2022 English Wikipedia.
            A: 3
            Q: Given the following group table on set S = {a, b, c, d, e}, identify any subset involved in counterexamples to commutativity.
            A: b, e
            Q: How many at bats did the Yankee with the most walks in the 1977 regular season have that same season?,
            A: 519
            """
        )

    def select_model(self, provider: str):
        if provider == "openai":
            return LiteLLMModel(model_id=OPENAI_MODEL_NAME, api_key=os.getenv("OPENAI_API_KEY"))
        elif provider == "groq":
            return LiteLLMModel(model_id=GROQ_MODEL_NAME, api_key=os.getenv("GROQ_API_KEY"))
        elif provider == "deepseek":
            return LiteLLMModel(model_id=DEEPSEEK_MODEL_NAME, api_key=os.getenv("DEEPSEEK_API_KEY"))
        elif provider == "hf":
            return InferenceClientModel()
        else:
            return LiteLLMModel(model_id=GEMINI_MODEL_NAME, api_key=os.getenv("GOOGLE_API_KEY"))

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

        return final_str

    def evaluate_random_questions(self, csv_path: str = "gaia_extracted.csv", sample_size: int = 3, show_steps: bool = True):
        import pandas as pd
        from rich.table import Table
        from rich.console import Console

        df = pd.read_csv(csv_path)
        if not {"question", "answer"}.issubset(df.columns):
            print("CSV must contain 'question' and 'answer' columns.")
            print("Found columns:", df.columns.tolist())
            return

        samples = df.sample(n=sample_size)
        records = []
        correct_count = 0

        for _, row in samples.iterrows():
            taskid = row["taskid"].strip()
            question = row["question"].strip()
            expected = str(row['answer']).strip()
            agent_answer = self("taskid: " + taskid + ",\nquestion: " + question).strip()

            is_correct = (expected == agent_answer)
            correct_count += is_correct
            records.append((question, expected, agent_answer, "✓" if is_correct else "✗"))

            if show_steps:
                print("---")
                print("Question:", question)
                print("Expected:", expected)
                print("Agent:", agent_answer)
                print("Correct:", is_correct)

        # Print result table
        console = Console()
        table = Table(show_lines=True)
        table.add_column("Question", overflow="fold")
        table.add_column("Expected")
        table.add_column("Agent")
        table.add_column("Correct")

        for question, expected, agent_ans, correct in records:
            table.add_row(question, expected, agent_ans, correct)

        console.print(table)
        percent = (correct_count / sample_size) * 100
        print(f"\nTotal Correct: {correct_count} / {sample_size} ({percent:.2f}%)")


if __name__ == "__main__":
    args = sys.argv[1:]
    if not args or args[0] in {"-h", "--help"}:
        print("Usage: python agent.py [question | dev]")
        print(" - Provide a question to get a GAIA-style answer.")
        print(" - Use 'dev' to evaluate 3 random GAIA questions from gaia_qa.csv.")
        sys.exit(0)

    q = " ".join(args)
    agent = BasicAgent()
    if q == "dev":
        agent.evaluate_random_questions()
    else:
        print(agent(q))