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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()
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