Update app.py

app.py

@@ -1,35 +1,71 @@
 import gradio as gr
+from gradio.inputs import File
+from gradio.outputs import Text, Image
+import os
+import torch
+from PIL import Image as PilImage
+from torchvision.transforms import ToTensor
 
-def process_and_optimize_blueprint(file, requirements):
-    # Instantiate your AI model
-    ai_optimizer = gr.Interface.load("models/facebook/dino-vitb16")
+# Load the DINO model
+ai_optimizer = gr.Interface.load("models/facebook/dino-vitb16")
 
-    # Process the blueprint
-    blueprint_features = ai_optimizer.process_blueprint(file)
+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
 
-    # Process the requirements
-    requirements_features = ai_optimizer.process_requirements(requirements)
+def load_model():
+    """
+    This function should load your model. Here, we're returning the DINO model.
+    """
+    model = ai_optimizer
+    return model
 
-    # Generate optimizations
-    optimizations = ai_optimizer.optimize_design(blueprint_features, requirements_features)
+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
 
-    # Estimate costs
-    cost_estimate = ai_optimizer.estimate_costs(blueprint_features, requirements_features)
+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
 
-    return optimizations, cost_estimate
+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
 
-# Define the Gradio interface
 iface = gr.Interface(
-    fn=process_and_optimize_blueprint,
-    inputs=[
-        gr.inputs.File(label="Upload Blueprint"),
-        gr.inputs.Textbox(lines=5, label="Enter Requirements"),
-    ],
-    outputs=[
-        gr.outputs.Textbox(label="Optimizations"),
-        gr.outputs.Textbox(label="Cost Estimate"),
-    ],
+    fn=analyze_blueprint,
+    inputs=File(label="Input Blueprint Image"),
+    outputs=[Text(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."
 )
 
-# Launch the Gradio interface
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()