import gradio as gr
from gradio.inputs import File
from gradio.outputs import Textbox, Image
import os
import torch
from PIL import Image as PilImage
from torchvision.transforms import ToTensor

# Load the DINO model
ai_optimizer = gr.Interface.load("models/facebook/dino-vitb16")

def load_data(image_file):
    """
    This function should load the data from the provided image file.
    This will convert the image file into a PIL Image.
    """
    image = PilImage.open(image_file)
    return image

def load_model():
    """
    This function should load your model. Here, we're returning the DINO model.
    """
    model = ai_optimizer
    return model

def generate_text_report(analysis):
    """
    This function should generate a text report based on the analysis made by your model.
    Here, we're simply returning a placeholder.
    """
    text_report = "your text report"
    return text_report

def generate_updated_blueprint_image(analysis):
    """
    This function should generate an image based on the analysis made by your model.
    Here, we're simply returning a placeholder.
    """
    image = "your image"
    return image

def analyze_blueprint(image_file):
    image = load_data(image_file)
    model = load_model()

    # Transform the image to tensor
    transform = ToTensor()
    image_tensor = transform(image)

    # Add an extra dimension at the start for the batch size
    image_tensor = image_tensor.unsqueeze(0)

    # Pass the image through the model
    analysis = model.predict(image_tensor)

    text_report = generate_text_report(analysis)
    updated_blueprint = generate_updated_blueprint_image(analysis)

    return text_report, updated_blueprint

iface = gr.Interface(
    fn=analyze_blueprint,
    inputs=File(label="Input Blueprint Image"),
    outputs=[Textbox(label="Analysis and Cost Estimation"), Image(plot=True, label="Updated Blueprint")],
    title="Blueprint Analyzer",
    description="Upload a blueprint image and get back an analysis and cost estimation."
)

if __name__ == "__main__":
    iface.launch()