from PIL import Image
# import torch
import os
import gradio as gr

# Below is the code refactored into a Python class for better modularity and reusability.


import torch
from transformers import TextStreamer

class FloorPlanAnalyzer:
    def __init__(self, model_path, load_in_4bit=True, gradient_checkpointing="unsloth", device="cpu"):
        """
        Initializes the FloorPlanAnalyzer with the specified model and configuration.
        """
        from unsloth import FastVisionModel  # Assuming unsloth package is installed

        self.device = device
        self.model, self.tokenizer = FastVisionModel.from_pretrained(
            model_path,
            load_in_4bit=load_in_4bit,
            use_gradient_checkpointing=gradient_checkpointing,
        )
        FastVisionModel.for_inference(self.model)

    def prepare_input(self, image_path, instruction):
        """
        Prepares the input for the model by loading the image and applying the chat template.

        Args:
            image_path (str): Path to the floor plan image.
            instruction (str): Instruction text to guide the analysis.

        Returns:
            torch.Tensor: Processed inputs for the model.
        """
        # Load image
        image = Image.open(image_path).convert("RGB")

        # Create message template
        messages = [
            {"role": "user", "content": [
                {"type": "image"},
                {"type": "text", "text": instruction}
            ]}
        ]

        # Generate input text
        input_text = self.tokenizer.apply_chat_template(messages, add_generation_prompt=True)

        # Tokenize and prepare inputs
        inputs = self.tokenizer(
            image,
            input_text,
            add_special_tokens=False,
            return_tensors="pt",
        ).to(self.device)

        return inputs

    def analyze(self, image_path, instruction, max_new_tokens=512, temperature=1.5, min_p=0.1):
        """
        Analyzes the floor plan based on the provided instruction.

        Args:
            image_path (str): Path to the floor plan image.
            instruction (str): Instruction guiding the analysis.
            max_new_tokens (int): Maximum number of tokens to generate.
            temperature (float): Sampling temperature for generation.
            min_p (float): Minimum probability for nucleus sampling.

        Returns:
            str: The generated output from the model.
        """
        # Prepare inputs
        inputs = self.prepare_input(image_path, instruction)

        # Set up text streamer
        text_streamer = TextStreamer(self.tokenizer, skip_prompt=True)

        # Generate output
        output = self.model.generate(
            **inputs,
            streamer=text_streamer,
            max_new_tokens=max_new_tokens,
            use_cache=True,
            temperature=temperature,
            min_p=min_p,
        )

        return output




# Instantiate the FloorPlanAnalyzer
model_path = "./model/"
analyzer = FloorPlanAnalyzer(model_path=model_path)


# Sample images for Gradio examples
# Define sample images for user convenience
# Define sample images and instructions
sample_images = [
    ["./samples/10_2.jpg", "You are an expert in architecture and interior design. Analyze the floor plan image and describe accurately the key features, room count, layout, and any other important details you observe."],
    ["./samples/10_10.jpg", "You are an expert in architecture and interior design. Analyze the floor plan image and describe accurately the key features, room count, layout, and any other important details you observe."],
    ["./samples/0_10.jpg", "You are an expert in architecture and interior design. Analyze the floor plan image and describe accurately the key features, room count, layout, and any other important details you observe."],
    ["./samples/2_12.jpg", "You are an expert in architecture and interior design. Analyze the floor plan image and describe accurately the key features, room count, layout, and any other important details you observe."]
]

# Ensure samples directory exists
os.makedirs("samples", exist_ok=True)

# Save some dummy sample images if they don't exist (you should replace these with actual images)
for i, sample in enumerate(sample_images):
    image_path = sample[0]
    if not os.path.exists(image_path):
        img = Image.new("RGB", (224, 224), color=(i * 50, i * 50, i * 50))
        img.save(image_path)

# Gradio prediction function
def predict_image(image, instruction):
    """
    Processes the uploaded image and instruction through the FloorPlanAnalyzer.

    Args:
        image (PIL.Image.Image): The uploaded floor plan image.
        instruction (str): The user-provided instruction.

    Returns:
        str: The generated output description.
    """
    return analyzer.analyze(image, instruction)

gr_interface = gr.Interface(
    fn=predict_image,
    inputs=[
        gr.Image(type="pil", label="Upload Floor Plan Image"),
        gr.Textbox(
            label="Instruction Text",
            value="""You are an expert in architecture and interior design. Analyze the floor plan image and describe accurately the key features, room count, layout, and any other important details you observe."""
        )
    ],
    outputs=gr.Textbox(label="Analysis Result"),
    title="Floor Plan Analyzer",
    description="Upload a floor plan image and provide instructions to analyze it. Get detailed insights into the layout and design.",
    examples=sample_images  # Add the examples here
)

# # Gradio UI setup with examples
# gr_interface = gr.Interface(
#     fn=predict_image, 
#     inputs=gr.Image(type="pil"),  # Updated to gr.Image for image input
#     outputs=[gr.Image(type="pil"), gr.Textbox()],  # Updated to gr.Image and gr.Textbox
#     title="House CAD Design Object Detection", 
#     description="Upload a CAD design image of a house to detect objects with bounding boxes and probabilities.",
#     examples=sample_images  # Add the examples here
# )

# Launch the Gradio interface if run as main
if __name__ == "__main__":
    gr_interface.launch()