Create app.py

app.py

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+import gradio as gr
+import joblib
+import cv2
+import numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+
+# Define paths to models and load the scaler
+model_paths = {
+  "Regressor_decision_tree": "multioutput_regressor_decision_tree.joblib",
+  "Regressor_ridge": "regressor_ridge.joblib",
+  "Regressor_elastic_net": "elastic_net_model.joblib",
+  "NN_6_Layers": "NN_Layers_6.keras",
+  "CNN": "cnn_model_bigger.keras",
+  "CNN_with_reductions": "cnn_model_bigger_with_reductions.keras"
+}
+scaler = joblib.load("scaler.joblib")
+
+# Function to load models based on file extension
+def load_model_by_type(path):
+  if path.endswith('.joblib'):
+    return joblib.load(path)
+  elif path.endswith('.keras'):
+    return load_model(path)
+  else:
+    raise ValueError(f"Unsupported file extension for file {path}")
+
+# Load models with appropriate method
+models = {name: load_model_by_type(path) for name, path in model_paths.items()}
+
+
+def detect_objects(image, model_name):
+  model = models[model_name]
+  
+  # Assuming Gradio passes image as a numpy array and checking if conversion is needed
+  if image.ndim == 3 and image.shape[2] == 3:  # If the image is RGB
+    image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)  # Convert to grayscale using OpenCV
+  else:
+    image_gray = image  # Use the image as is if already grayscale
+  
+  # Check if the model requires CNN specific preprocessing
+  if model_name in ["CNN_model", "CNN_with_reductions"]:
+    image_processed = np.array(image_gray)
+    image_processed = image_processed.reshape(1, image_gray.shape[0], image_gray.shape[1], 1)
+    image_processed = image_processed.astype('float32')
+    image_processed /= 255 # Normalize pixel values
+  else:
+    # Assuming other models might expect flattened, scaled input
+    image_processed = image_gray.flatten().reshape(1, -1)
+    image_processed = scaler.transform(image_processed)
+
+  # Make prediction
+  predictions = model.predict(image_processed)
+  x, y, width, height = predictions[0]
+
+  # Draw bounding box on a copy of the original image (converted back to RGB for color drawing)
+  original_image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Ensure image is in RGB
+  cv2.rectangle(original_image_rgb, (int(x), int(y)), (int(x + width), int(y + height)), (0, 255, 0), 2)
+
+  return Image.fromarray(original_image_rgb)
+
+# Gradio interface setup
+iface = gr.Interface(
+  fn=detect_objects,
+  inputs=[gr.components.Image(), gr.components.Dropdown(list(model_paths.keys()))],
+  outputs=gr.components.Image(),
+  title="Object Detection",
+  description="Select a model and upload an image to detect objects."
+)
+
+iface.launch(show_error=True, share=True, debug=True)