Ricdeq commited on
Commit
147235c
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1 Parent(s): 7b7e637

Update app.py

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  1. app.py +60 -24
app.py CHANGED
@@ -1,35 +1,71 @@
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  import gradio as gr
 
 
 
 
 
 
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- def process_and_optimize_blueprint(file, requirements):
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- # Instantiate your AI model
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- ai_optimizer = gr.Interface.load("models/facebook/dino-vitb16")
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- # Process the blueprint
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- blueprint_features = ai_optimizer.process_blueprint(file)
 
 
 
 
 
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- # Process the requirements
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- requirements_features = ai_optimizer.process_requirements(requirements)
 
 
 
 
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- # Generate optimizations
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- optimizations = ai_optimizer.optimize_design(blueprint_features, requirements_features)
 
 
 
 
 
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- # Estimate costs
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- cost_estimate = ai_optimizer.estimate_costs(blueprint_features, requirements_features)
 
 
 
 
 
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- return optimizations, cost_estimate
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- # Define the Gradio interface
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  iface = gr.Interface(
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- fn=process_and_optimize_blueprint,
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- inputs=[
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- gr.inputs.File(label="Upload Blueprint"),
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- gr.inputs.Textbox(lines=5, label="Enter Requirements"),
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- ],
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- outputs=[
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- gr.outputs.Textbox(label="Optimizations"),
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- gr.outputs.Textbox(label="Cost Estimate"),
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- ],
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  )
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- # Launch the Gradio interface
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- iface.launch()
 
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  import gradio as gr
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+ from gradio.inputs import File
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+ from gradio.outputs import Text, Image
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+ import os
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+ import torch
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+ from PIL import Image as PilImage
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+ from torchvision.transforms import ToTensor
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+ # Load the DINO model
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+ ai_optimizer = gr.Interface.load("models/facebook/dino-vitb16")
 
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+ def load_data(image_file):
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+ """
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+ This function should load the data from the provided image file.
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+ This will convert the image file into a PIL Image.
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+ """
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+ image = PilImage.open(image_file)
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+ return image
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+ def load_model():
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+ """
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+ This function should load your model. Here, we're returning the DINO model.
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+ """
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+ model = ai_optimizer
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+ return model
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+ def generate_text_report(analysis):
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+ """
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+ This function should generate a text report based on the analysis made by your model.
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+ Here, we're simply returning a placeholder.
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+ """
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+ text_report = "your text report"
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+ return text_report
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+ def generate_updated_blueprint_image(analysis):
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+ """
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+ This function should generate an image based on the analysis made by your model.
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+ Here, we're simply returning a placeholder.
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+ """
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+ image = "your image"
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+ return image
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+ def analyze_blueprint(image_file):
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+ image = load_data(image_file)
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+ model = load_model()
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+
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+ # Transform the image to tensor
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+ transform = ToTensor()
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+ image_tensor = transform(image)
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+
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+ # Add an extra dimension at the start for the batch size
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+ image_tensor = image_tensor.unsqueeze(0)
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+
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+ # Pass the image through the model
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+ analysis = model.predict(image_tensor)
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+
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+ text_report = generate_text_report(analysis)
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+ updated_blueprint = generate_updated_blueprint_image(analysis)
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+
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+ return text_report, updated_blueprint
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  iface = gr.Interface(
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+ fn=analyze_blueprint,
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+ inputs=File(label="Input Blueprint Image"),
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+ outputs=[Text(label="Analysis and Cost Estimation"), Image(plot=True, label="Updated Blueprint")],
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+ title="Blueprint Analyzer",
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+ description="Upload a blueprint image and get back an analysis and cost estimation."
 
 
 
 
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  )
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+ if __name__ == "__main__":
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+ iface.launch()