import os
import json
import gc
import time
import traceback
from typing import Dict, List, Optional, Tuple, Callable, Any

import torch
import gradio as gr
import supervision as sv
from PIL import Image

# Try to import optional dependencies
try:
    from transformers import (
        AutoModelForCausalLM,
        AutoTokenizer,
        AutoModelForImageTextToText,
        AutoProcessor,
        BitsAndBytesConfig,
    )
except Exception:
    AutoModelForCausalLM = None
    AutoTokenizer = None
    AutoModelForImageTextToText = None
    AutoProcessor = None
    BitsAndBytesConfig = None

# Import RF-DETR (assumes it's in the same directory or installed)
try:
    from rfdetr import RFDETRMedium
except ImportError:
    print("Warning: RF-DETR not found. Please ensure it's properly installed.")
    RFDETRMedium = None

# ============================================================================
# Configuration for Hugging Face Spaces
# ============================================================================

class SpacesConfig:
    """Configuration optimized for Hugging Face Spaces."""

    def __init__(self):
        self.settings = {
            'results_dir': '/tmp/results',
            'checkpoint': None,
            'resolution': 576,
            'threshold': 0.7,
            'use_llm': True,
            'llm_model_id': 'google/medgemma-4b-it',
            'llm_max_new_tokens': 200,
            'llm_temperature': 0.2,
            'llm_4bit': True,
            'enable_caching': True,
            'max_cache_size': 100,
        }

    def get(self, key: str, default: Any = None) -> Any:
        return self.settings.get(key, default)

# ============================================================================
# Memory Management (simplified for Spaces)
# ============================================================================

class MemoryManager:
    """Simplified memory management for Spaces."""

    def __init__(self):
        self.memory_thresholds = {
            'gpu_warning': 0.8,
            'system_warning': 0.85,
        }

    def cleanup_memory(self, force: bool = False) -> None:
        """Perform memory cleanup."""
        try:
            gc.collect()
            if torch and torch.cuda.is_available():
                torch.cuda.empty_cache()
                torch.cuda.synchronize()
        except Exception as e:
            print(f"Memory cleanup error: {e}")

# Global memory manager
memory_manager = MemoryManager()

# ============================================================================
# Model Loading
# ============================================================================

def find_checkpoint() -> Optional[str]:
    """Find RF-DETR checkpoint in various locations."""
    candidates = [
        "rf-detr-medium.pth",  # Current directory
        "/tmp/results/checkpoint_best_total.pth",
        "/tmp/results/checkpoint_best_ema.pth",
        "/tmp/results/checkpoint_best_regular.pth",
        "/tmp/results/checkpoint.pth",
    ]

    for path in candidates:
        if os.path.isfile(path):
            return path
    return None

def load_model(checkpoint_path: str, resolution: int):
    """Load RF-DETR model."""
    if RFDETRMedium is None:
        raise RuntimeError("RF-DETR not available. Please install it properly.")

    model = RFDETRMedium(pretrain_weights=checkpoint_path, resolution=resolution)
    try:
        model.optimize_for_inference()
    except Exception:
        pass
    return model

# ============================================================================
# LLM Integration
# ============================================================================

class TextGenerator:
    """Simplified text generator for Spaces."""

    def __init__(self, model_id: str, max_tokens: int = 200, temperature: float = 0.2):
        self.model_id = model_id
        self.max_tokens = max_tokens
        self.temperature = temperature
        self.model = None
        self.tokenizer = None
        self.processor = None
        self.is_multimodal = False

    def load_model(self):
        """Load the LLM model."""
        if self.model is not None:
            return

        if (AutoModelForCausalLM is None and AutoModelForImageTextToText is None):
            raise RuntimeError("Transformers not available")

        # Clear memory before loading
        memory_manager.cleanup_memory()

        print(f"Loading model: {self.model_id}")

        model_kwargs = {
            "device_map": "auto",
            "low_cpu_mem_usage": True,
        }

        if torch and torch.cuda.is_available():
            model_kwargs["torch_dtype"] = torch.bfloat16

        # Use 4-bit quantization if available
        if BitsAndBytesConfig is not None:
            try:
                compute_dtype = torch.bfloat16 if torch and torch.cuda.is_available() else torch.float16
                model_kwargs["quantization_config"] = BitsAndBytesConfig(
                    load_in_4bit=True,
                    bnb_4bit_compute_dtype=compute_dtype,
                    bnb_4bit_use_double_quant=True,
                    bnb_4bit_quant_type="nf4"
                )
                model_kwargs["torch_dtype"] = compute_dtype
            except Exception:
                pass

        # Check if it's a multimodal model
        is_multimodal = "medgemma" in self.model_id.lower()

        if is_multimodal and AutoModelForImageTextToText is not None and AutoProcessor is not None:
            self.processor = AutoProcessor.from_pretrained(self.model_id)
            self.model = AutoModelForImageTextToText.from_pretrained(self.model_id, **model_kwargs)
            self.is_multimodal = True
        elif AutoModelForCausalLM is not None and AutoTokenizer is not None:
            self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
            self.model = AutoModelForCausalLM.from_pretrained(self.model_id, **model_kwargs)
            self.is_multimodal = False
        else:
            raise RuntimeError("Required model classes not available")

        print("✓ Model loaded successfully")

    def generate(self, text: str, image: Optional[Image.Image] = None) -> str:
        """Generate text using the loaded model."""
        self.load_model()

        if self.model is None:
            return f"[Model not loaded: {text}]"

        try:
            # Create messages
            system_text = "You are a concise medical assistant. Provide a brief, clear summary of detection results. Avoid repetition and be direct. Do not give medical advice."
            user_text = f"Summarize these detection results in 3 clear sentences:\n\n{text}"

            if self.is_multimodal:
                # Multimodal model
                user_content = [{"type": "text", "text": user_text}]
                if image is not None:
                    user_content.append({"type": "image", "image": image})

                messages = [
                    {"role": "system", "content": [{"type": "text", "text": system_text}]},
                    {"role": "user", "content": user_content},
                ]

                inputs = self.processor.apply_chat_template(
                    messages,
                    add_generation_prompt=True,
                    tokenize=True,
                    return_dict=True,
                    return_tensors="pt",
                )

                if torch:
                    inputs = inputs.to(self.model.device, dtype=torch.bfloat16)

                with torch.inference_mode():
                    generation = self.model.generate(
                        **inputs,
                        max_new_tokens=self.max_tokens,
                        do_sample=self.temperature > 0,
                        temperature=max(0.01, self.temperature) if self.temperature > 0 else None,
                        use_cache=False,
                    )

                input_len = inputs["input_ids"].shape[-1]
                generation = generation[0][input_len:]
                decoded = self.processor.decode(generation, skip_special_tokens=True)
                return decoded.strip()

            else:
                # Text-only model
                messages = [
                    {"role": "system", "content": system_text},
                    {"role": "user", "content": user_text},
                ]

                inputs = self.tokenizer.apply_chat_template(
                    messages,
                    add_generation_prompt=True,
                    tokenize=True,
                    return_dict=True,
                    return_tensors="pt",
                )

                inputs = inputs.to(self.model.device)

                with torch.inference_mode():
                    generation = self.model.generate(
                        **inputs,
                        max_new_tokens=self.max_tokens,
                        do_sample=self.temperature > 0,
                        temperature=max(0.01, self.temperature) if self.temperature > 0 else None,
                        use_cache=False,
                    )

                input_len = inputs["input_ids"].shape[-1]
                generation = generation[0][input_len:]
                decoded = self.tokenizer.decode(generation, skip_special_tokens=True)
                return decoded.strip()

        except Exception as e:
            error_msg = f"[Generation error: {e}]"
            print(f"Generation error: {traceback.format_exc()}")
            return f"{error_msg}\n\n{text}"

# ============================================================================
# Application State
# ============================================================================

class AppState:
    """Application state for Spaces."""

    def __init__(self):
        self.config = SpacesConfig()
        self.model = None
        self.class_names = None
        self.text_generator = None

    def load_model(self):
        """Load the detection model."""
        if self.model is not None:
            return

        checkpoint = find_checkpoint()
        if not checkpoint:
            raise FileNotFoundError(
                "No RF-DETR checkpoint found. Please upload rf-detr-medium.pth to your Space."
            )

        print(f"Loading RF-DETR from: {checkpoint}")
        self.model = load_model(checkpoint, self.config.get('resolution'))

        # Try to load class names
        try:
            results_json = "/tmp/results/results.json"
            if os.path.isfile(results_json):
                with open(results_json, 'r') as f:
                    data = json.load(f)
                classes = []
                for split in ("valid", "test", "train"):
                    if "class_map" in data and split in data["class_map"]:
                        for item in data["class_map"][split]:
                            name = item.get("class")
                            if name and name != "all" and name not in classes:
                                classes.append(name)
                self.class_names = classes if classes else None
        except Exception:
            pass

        print("✓ RF-DETR model loaded")

    def get_text_generator(self, model_size: str = "4B") -> TextGenerator:
        """Get or create text generator."""
        # Determine model ID based on size selection
        model_id = 'google/medgemma-27b-it' if model_size == "27B" else 'google/medgemma-4b-it'

        # Check if we need to create a new generator for different model size
        if (self.text_generator is None or
            hasattr(self.text_generator, 'model_id') and
            self.text_generator.model_id != model_id):

            max_tokens = self.config.get('llm_max_new_tokens')
            temperature = self.config.get('llm_temperature')

            self.text_generator = TextGenerator(model_id, max_tokens, temperature)
        return self.text_generator

# ============================================================================
# UI and Inference
# ============================================================================

def create_detection_interface():
    """Create the Gradio interface."""

    # Color palette for annotations
    COLOR_PALETTE = sv.ColorPalette.from_hex([
        "#ffff00", "#ff9b00", "#ff66ff", "#3399ff", "#ff66b2",
        "#ff8080", "#b266ff", "#9999ff", "#66ffff", "#33ff99",
        "#66ff66", "#99ff00",
    ])

    def annotate_image(image: Image.Image, threshold: float, model_size: str = "4B") -> Tuple[Image.Image, str]:
        """Process an image and return annotated version with description."""

        if image is None:
            return None, "Please upload an image."

        try:
            # Load model if needed
            app_state.load_model()

            # Run detection
            detections = app_state.model.predict(image, threshold=threshold)

            # Annotate image
            bbox_annotator = sv.BoxAnnotator(color=COLOR_PALETTE, thickness=2)
            label_annotator = sv.LabelAnnotator(text_scale=0.5, text_color=sv.Color.BLACK)

            labels = []
            for i in range(len(detections)):
                class_id = int(detections.class_id[i]) if detections.class_id is not None else None
                conf = float(detections.confidence[i]) if detections.confidence is not None else 0.0

                if app_state.class_names and class_id is not None:
                    if 0 <= class_id < len(app_state.class_names):
                        label_name = app_state.class_names[class_id]
                    else:
                        label_name = str(class_id)
                else:
                    label_name = str(class_id) if class_id is not None else "object"

                labels.append(f"{label_name} {conf:.2f}")

            annotated = image.copy()
            annotated = bbox_annotator.annotate(annotated, detections)
            annotated = label_annotator.annotate(annotated, detections, labels)

            # Generate description
            description = f"Found {len(detections)} detections above threshold {threshold}:\n\n"

            if len(detections) > 0:
                counts = {}
                for i in range(len(detections)):
                    class_id = int(detections.class_id[i]) if detections.class_id is not None else None
                    if app_state.class_names and class_id is not None:
                        if 0 <= class_id < len(app_state.class_names):
                            name = app_state.class_names[class_id]
                        else:
                            name = str(class_id)
                    else:
                        name = str(class_id) if class_id is not None else "object"
                    counts[name] = counts.get(name, 0) + 1

                for name, count in counts.items():
                    description += f"- {count}× {name}\n"

                # Use LLM for description if enabled
                if app_state.config.get('use_llm'):
                    try:
                        generator = app_state.get_text_generator(model_size)
                        llm_description = generator.generate(description, image=annotated)
                        description = llm_description
                    except Exception as e:
                        description = f"[LLM error: {e}]\n\n{description}"
            else:
                description += "No objects detected above the confidence threshold."

            return annotated, description

        except Exception as e:
            error_msg = f"Error processing image: {str(e)}"
            print(f"Processing error: {traceback.format_exc()}")
            return None, error_msg

    # Create the interface
    with gr.Blocks(title="Medical Image Analysis", theme=gr.themes.Soft()) as demo:
        gr.Markdown("# 🏥 Medical Image Analysis")
        gr.Markdown("Upload a medical image to detect and analyze findings using AI.")

        with gr.Row():
            with gr.Column():
                input_image = gr.Image(type="pil", label="Upload Image", height=400)
                threshold_slider = gr.Slider(
                    minimum=0.1,
                    maximum=1.0,
                    value=0.7,
                    step=0.05,
                    label="Confidence Threshold",
                    info="Higher values = fewer but more confident detections"
                )

                model_size_radio = gr.Radio(
                    choices=["4B", "27B"],
                    value="4B",
                    label="MedGemma Model Size",
                    info="4B: Faster, less memory | 27B: More accurate, more memory"
                )

                analyze_btn = gr.Button("🔍 Analyze Image", variant="primary")

            with gr.Column():
                output_image = gr.Image(type="pil", label="Results", height=400)
                output_text = gr.Textbox(
                    label="Analysis Results",
                    lines=8,
                    max_lines=15,
                    show_copy_button=True
                )

        # Wire up the interface
        analyze_btn.click(
            fn=annotate_image,
            inputs=[input_image, threshold_slider, model_size_radio],
            outputs=[output_image, output_text]
        )

        # Also run when image is uploaded
        input_image.change(
            fn=annotate_image,
            inputs=[input_image, threshold_slider, model_size_radio],
            outputs=[output_image, output_text]
        )

        # Footer
        gr.Markdown("---")
        gr.Markdown("*Powered by RF-DETR and MedGemma • Built for Hugging Face Spaces*")

    return demo

# ============================================================================
# Main Application
# ============================================================================

# Global app state
app_state = AppState()

def main():
    """Main entry point for the Spaces app."""
    print("🚀 Starting Medical Image Analysis App")

    # Ensure results directory exists
    os.makedirs(app_state.config.get('results_dir'), exist_ok=True)

    # Create and launch the interface
    demo = create_detection_interface()

    # Launch with Spaces-optimized settings
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,  # Spaces handles this
        show_error=True,
        show_api=False,
    )

if __name__ == "__main__":
    main()