import base64
import json
import re
from io import BytesIO
from typing import Tuple, List, Optional, Dict, Any, Type
import time

from PIL import Image
from langchain_core.messages import HumanMessage, BaseMessage
from hf_chat import HuggingFaceChat
from mapcrunch_controller import MapCrunchController

# The "Golden" Prompt (v7): add more descprtions in context and task
AGENT_PROMPT_TEMPLATE = """
**Mission:** You are an expert geo-location agent. Your goal is to pinpoint our position in as few moves as possible.

**Current Status**
• Remaining Steps: {remaining_steps}  
• Actions You Can Take *this* turn: {available_actions}

────────────────────────────────
**Core Principles**

1.  **Observe → Orient → Act**  
    Start each turn with a structured three-part reasoning block:  
    **(1) Visual Clues —** plainly describe what you see (signs, text language, road lines, vegetation, building styles, vehicles, terrain, weather, etc.).  
    **(2) Potential Regions —** list the most plausible regions/countries those clues suggest.  
    **(3) Most Probable + Plan —** pick the single likeliest region and explain the next action (move/pan or guess).  

2.  **Navigate with Labels:**  
    - `MOVE_FORWARD` follows the green **UP** arrow.  
    - `MOVE_BACKWARD` follows the red **DOWN** arrow.  
    - No arrow ⇒ you cannot move that way.

3.  **Efficient Exploration:**  
    - **Pan Before You Move:** At fresh spots/intersections, use `PAN_LEFT` / `PAN_RIGHT` first.  
    - After ~2 or 3 fruitless moves in repetitive scenery, turn around.

4.  **Be Decisive:** A unique, definitive clue (full address, rare town name, etc.) ⇒ `GUESS` immediately.

5.  **Final-Step Rule**
    - If **Remaining Steps = 1**, you **MUST** `GUESS` with coordinates.
    - **NO EXCEPTIONS**: Even with limited clues, provide your best estimate.
    - **ALWAYS provide lat/lon numbers** - educated guesses are mandatory.

────────────────────────────────
**Context & Task:**
Analyze your full journey history and current view, apply the Core Principles, and decide your next action in the required JSON format.

**Action History**
{history_text}

────────────────────────────────
**JSON Output Format:**More actions
Your response MUST be a valid JSON object wrapped in ```json ... ```.
- For exploration: `{{"reasoning": "...", "action_details": {{"action": "ACTION_NAME"}} }}`
- For the final guess: `{{"reasoning": "...", "action_details": {{"action": "GUESS", "lat": <float>, "lon": <float>}} }}`
"""

BENCHMARK_PROMPT = """
Analyze the image and determine its geographic coordinates.
1.  Describe visual clues.
2.  Suggest potential regions.
3.  State your most probable location.
4.  Provide coordinates in the last line in this exact format: `Lat: XX.XXXX, Lon: XX.XXXX`
"""


class GeoBot:
    def __init__(
        self,
        model: Type,
        model_name: str,
        use_selenium: bool = True,
        headless: bool = False,
        temperature: float = 0.0,
    ):
        # Initialize model with temperature parameter
        model_kwargs = {
            "temperature": temperature,
        }

        # Handle different model types
        if model == HuggingFaceChat and HuggingFaceChat is not None:
            model_kwargs["model"] = model_name
        else:
            model_kwargs["model"] = model_name

        try:
            self.model = model(**model_kwargs)
        except Exception as e:
            raise ValueError(f"Failed to initialize model {model_name}: {e}")

        self.model_name = model_name
        self.temperature = temperature
        self.use_selenium = use_selenium
        self.controller = MapCrunchController(headless=headless)

    @staticmethod
    def pil_to_base64(image: Image.Image) -> str:
        buffered = BytesIO()
        image.thumbnail((1024, 1024))
        image.save(buffered, format="PNG")
        return base64.b64encode(buffered.getvalue()).decode("utf-8")

    def _create_message_with_history(
        self, prompt: str, image_b64_list: List[str]
    ) -> List[HumanMessage]:
        """Creates a message for the LLM that includes text and a sequence of images."""
        content = [{"type": "text", "text": prompt}]

        # Add the JSON format instructions right after the main prompt text
        content.append(
            {
                "type": "text",
                "text": '\n**JSON Output Format:**\nYour response MUST be a valid JSON object wrapped in ```json ... ```.\n- For exploration: `{{"reasoning": "...", "action_details": {{"action": "ACTION_NAME"}} }}`\n- For the final guess: `{{"reasoning": "...", "action_details": {{"action": "GUESS", "lat": <float>, "lon": <float>}} }}`',
            }
        )

        for b64_string in image_b64_list:
            content.append(
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/png;base64,{b64_string}"},
                }
            )
        return [HumanMessage(content=content)]

    def _create_llm_message(self, prompt: str, image_b64: str) -> List[HumanMessage]:
        """Original method for single-image analysis (benchmark)."""
        return [
            HumanMessage(
                content=[
                    {"type": "text", "text": prompt},
                    {
                        "type": "image_url",
                        "image_url": {"url": f"data:image/png;base64,{image_b64}"},
                    },
                ]
            )
        ]

    def _parse_agent_response(
        self, response: BaseMessage, verbose: bool = False
    ) -> Optional[Dict[str, Any]]:
        """
        Robustly parses JSON from the LLM response with detailed logging.
        """
        try:
            assert isinstance(response.content, str), "Response content is not a string"
            content = response.content.strip()
            if verbose:
                print(f"Raw AI response: {content[:200]}...")  # Show first 200 chars

            match = re.search(r"```json\s*(\{.*?\})\s*```", content, re.DOTALL)
            if match:
                json_str = match.group(1)
                print(f"Extracted JSON: {json_str}")
            else:
                json_str = content
                print("No JSON code block found, trying to parse entire content")

            parsed = json.loads(json_str)
            print(f"Successfully parsed JSON: {parsed}")
            return parsed

        except (json.JSONDecodeError, AttributeError) as e:
            print(f"✗ JSON parsing failed: {e}")
            print(f"Full response was:\n{response.content}")
            return None

    def init_history(self) -> List[Dict[str, Any]]:
        """Initialize an empty history list for agent steps."""
        return []

    def add_step_to_history(
        self,
        history: List[Dict[str, Any]],
        screenshot_b64: str,
        decision: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, Any]:
        """
        Add a step to the history with proper structure.
        Returns the step dictionary that was added.
        """
        step = {
            "screenshot_b64": screenshot_b64,
            "reasoning": decision.get("reasoning", "N/A") if decision else "N/A",
            "action_details": decision.get("action_details", {"action": "N/A"})
            if decision
            else {"action": "N/A"},
        }
        history.append(step)
        return step

    def generate_history_text(self, history: List[Dict[str, Any]]) -> str:
        """Generate formatted history text for prompt."""
        if not history:
            return "No history yet. This is the first step."

        history_text = ""
        for i, h in enumerate(history):
            history_text += f"--- History Step {i + 1} ---\n"
            history_text += f"Reasoning: {h.get('reasoning', 'N/A')}\n"
            history_text += (
                f"Action: {h.get('action_details', {}).get('action', 'N/A')}\n\n"
            )
        return history_text

    def get_history_images(self, history: List[Dict[str, Any]]) -> List[str]:
        """Extract image base64 strings from history."""
        return [h["screenshot_b64"] for h in history]

    def execute_agent_step(
        self,
        history: List[Dict[str, Any]],
        remaining_steps: int,
        current_screenshot_b64: str,
        available_actions: List[str],
    ) -> Optional[Dict[str, Any]]:
        """
        Execute a single agent step: generate prompt, get AI decision, return decision.
        This is the core step logic extracted for reuse.
        """
        history_text = self.generate_history_text(history)
        image_b64_for_prompt = self.get_history_images(history) + [
            current_screenshot_b64
        ]

        prompt = AGENT_PROMPT_TEMPLATE.format(
            remaining_steps=remaining_steps,
            history_text=history_text,
            available_actions=available_actions,
        )

        try:
            message = self._create_message_with_history(
                prompt, image_b64_for_prompt[-1:]
            )
            response = self.model.invoke(message)
            verbose = remaining_steps == 1
            decision = self._parse_agent_response(response, verbose)
        except Exception as e:
            print(f"Error during model invocation: {e}")
            decision = None

        if not decision:
            print(
                "Response parsing failed or model error. Using default recovery action: PAN_RIGHT."
            )
            decision = {
                "reasoning": "Recovery due to parsing failure or model error.",
                "action_details": {"action": "PAN_RIGHT"},
            }

        return decision

    def execute_action(self, action: str) -> bool:
        """
        Execute the given action using the controller.
        Returns True if action was executed, False if it was GUESS.
        """
        if action == "GUESS":
            return False
        elif action == "MOVE_FORWARD":
            self.controller.move("forward")
        elif action == "MOVE_BACKWARD":
            self.controller.move("backward")
        elif action == "PAN_LEFT":
            self.controller.pan_view("left")
        elif action == "PAN_RIGHT":
            self.controller.pan_view("right")
        return True

    def run_agent_loop(
        self, max_steps: int = 10, step_callback=None
    ) -> Optional[Tuple[float, float]]:
        """
        Agent loop with simple retry logic and clear error coordinates.
        """
        history = self.init_history()

        for step in range(max_steps, 0, -1):
            step_num = max_steps - step + 1
            print(f"\n--- Step {step_num}/{max_steps} ---")

            # Simple retry for screenshot
            screenshot_bytes = None
            for retry in range(3):
                try:
                    self.controller.setup_clean_environment()
                    self.controller.label_arrows_on_screen()
                    screenshot_bytes = self.controller.take_street_view_screenshot()
                    if screenshot_bytes:
                        break
                    print(f"Screenshot retry {retry + 1}/3")
                except Exception as e:
                    print(f"Error in step {step_num}, retry {retry + 1}: {e}")
                    if retry < 2:
                        time.sleep(2)

            if not screenshot_bytes:
                print("Failed to get screenshot after retries")
                return -1.0, -1.0

            current_screenshot_b64 = self.pil_to_base64(
                image=Image.open(BytesIO(screenshot_bytes))
            )
            available_actions = self.controller.get_available_actions()
            print(f"Available actions: {available_actions}")

            # Get AI decision
            if step == 1:  # Final step - force guess
                decision = self._get_final_guess(
                    history, current_screenshot_b64, available_actions
                )
            else:
                decision = self.execute_agent_step(
                    history, step, current_screenshot_b64, available_actions
                )

            if not decision:
                print("No decision from AI, using fallback")
                decision = {
                    "reasoning": "AI decision failed",
                    "action_details": {
                        "action": "GUESS" if step == 1 else "PAN_RIGHT",
                        "lat": -1.0,
                        "lon": -1.0,
                    },
                }

            # UI callback
            step_info = {
                "step_num": step_num,
                "max_steps": max_steps,
                "remaining_steps": step,
                "screenshot_bytes": screenshot_bytes,
                "screenshot_b64": current_screenshot_b64,
                "available_actions": available_actions,
                "is_final_step": step == 1,
                "reasoning": decision.get("reasoning", "N/A"),
                "action_details": decision.get("action_details", {"action": "N/A"}),
                "history": history.copy(),
            }

            action_details = decision.get("action_details", {})
            action = action_details.get("action")
            print(f"AI Reasoning: {decision.get('reasoning', 'N/A')}")
            print(f"AI Action: {action}")

            if step_callback:
                try:
                    step_callback(step_info)
                except Exception as e:
                    print(f"UI callback error: {e}")

            # Add to history
            self.add_step_to_history(history, current_screenshot_b64, decision)

            # Execute action
            if action == "GUESS":
                lat = action_details.get("lat", -1.0)
                lon = action_details.get("lon", -1.0)
                print(f"Final guess: lat={lat}, lon={lon}")

                # Validate coordinates
                try:
                    lat_f, lon_f = float(lat), float(lon)
                    if -90 <= lat_f <= 90 and -180 <= lon_f <= 180:
                        return lat_f, lon_f
                except (ValueError, TypeError):
                    pass

                print("Invalid coordinates, returning error values")
                return -1.0, -1.0
            else:
                self.execute_action(action)

        print("Max steps reached without guess")
        return -1.0, -1.0

    def _get_final_guess(self, history, screenshot_b64, available_actions):
        """Get final guess from AI with simple retry."""
        for retry in range(2):
            try:
                # If retry > 0, use a force prompt to ensure the AI returns a GUESS with coordinates.
                if retry > 0:
                    history_text = self.generate_history_text(history)
                    force_prompt = f"""**FINAL STEP - MANDATORY GUESS**
You MUST return GUESS with coordinates. No other action allowed.
Remaining Steps: 1
Journey history: {history_text}
Provide your best lat/lon estimate based on all observed clues.
**MANDATORY JSON Format:**
{{"reasoning": "your analysis", "action_details": {{"action": "GUESS", "lat": 45.0, "lon": 2.0}} }}"""

                    message = self._create_message_with_history(
                        force_prompt, [screenshot_b64]
                    )
                    response = self.model.invoke(message)
                    decision = self._parse_agent_response(response)
                else:
                    decision = self.execute_agent_step(
                        history, 1, screenshot_b64, available_actions
                    )
                if (
                    decision
                    and decision.get("action_details", {}).get("action") == "GUESS"
                ):
                    return decision
                print(f"AI didn't return GUESS, retry {retry + 1}/2")
            except Exception as e:
                print(f"AI call failed, retry {retry + 1}/2: {e}")

            if retry == 0:
                time.sleep(1)

        # Fallback
        return {
            "reasoning": "AI failed to provide final guess after retries",
            "action_details": {"action": "GUESS", "lat": -1.0, "lon": -1.0},
        }

    def analyze_image(self, image: Image.Image) -> Optional[Tuple[float, float]]:
        image_b64 = self.pil_to_base64(image)
        message = self._create_llm_message(BENCHMARK_PROMPT, image_b64)

        try:
            response = self.model.invoke(message)
            print(f"\nLLM Response:\n{response.content}")
        except Exception as e:
            print(f"Error during image analysis: {e}")
            return None

        content = response.content.strip()
        last_line = ""
        for line in reversed(content.split("\n")):
            if "lat" in line.lower() and "lon" in line.lower():
                last_line = line
                break
        if not last_line:
            return None

        numbers = re.findall(r"[-+]?\d*\.\d+|\d+", last_line)
        if len(numbers) < 2:
            return None

        lat, lon = float(numbers[0]), float(numbers[1])
        return lat, lon

    def take_screenshot(self) -> Optional[Image.Image]:
        screenshot_bytes = self.controller.take_street_view_screenshot()
        if screenshot_bytes:
            return Image.open(BytesIO(screenshot_bytes))
        return None

    def close(self):
        if self.controller:
            self.controller.close()

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.close()