Update app.py

app.py

@@ -1,55 +1,55 @@
-import gradio as gr
-
-
-from src.inference import load_classifier, load_model, generate_images, convert_into_image, classify_image
-from src.models import ResUNetGenerator
-from src.explainer import GradCAM, preprocess_image
-
-# Loading Models
-classifier_path = 'models\\efficientnet_b1-epoch16-val_loss0.46_ft.ckpt'
-g_NP_checkpoint = 'models\\g_NP_best.ckpt'
-g_PN_checkpoint = 'models\\g_PN_best.ckpt'
-g_NP = load_model(g_NP_checkpoint, ResUNetGenerator(gf=32, channels=1))
-g_PN = load_model(g_PN_checkpoint, ResUNetGenerator(gf=32, channels=1))
-classifier = load_classifier(classifier_path)
-target_layer = classifier.model.features[-1]
-grad_cam = GradCAM(classifier, target_layer)
-
-
-def counterfactual_generation(input_image):
-
-    translated_images, recon_images = generate_images(input_image, classifier, g_PN, g_NP)
-    translated_images = convert_into_image(translated_images)
-    recon_images = convert_into_image(recon_images)
-    return translated_images, recon_images
-
-def image_classification(input_image):
-
-    result, target_class = classify_image(input_image, classifier=classifier)
-    input_tensor = preprocess_image(input_image)
-    cam = grad_cam.generate_cam(input_tensor, target_class)
-    cam_image = grad_cam.visualize_cam(cam, input_tensor)
-
-    return result, cam_image
-
-# Defining the components
-inputs1 = gr.Image(type="pil", format="png")
-inputs2 = gr.Image(type="pil", format="png")
-outputs1 = [gr.Image(type="pil", label="Translated Images", format="png"),
-           gr.Image(type="pil", label="Reconstructed Images", format="png")]
-
-outputs2 = [gr.Label(label="Classification Result"), gr.Image(label="Grad-CAM", format="png")]
-
-with gr.Blocks() as demo:
-    with gr.Tab("Counterfactual Generation"):
-        app1 = gr.Interface(fn=counterfactual_generation, inputs=inputs1, outputs=outputs1,
-                            title="Counterfactual Image Generation", allow_flagging="never",
-                            description="Generate counterfactual images to explain the classifier's decisions.")
-
-    with gr.Tab("Classification"):
-        app2 = gr.Interface(fn=image_classification, inputs=inputs2, outputs=outputs2,
-                            title="Image Classification", allow_flagging="never",
-                            description="Classify the input medical image and visualize Grad-CAM.")
-
-# Launch the app
+import gradio as gr
+
+
+from src.inference import load_classifier, load_model, generate_images, convert_into_image, classify_image
+from src.models import ResUNetGenerator
+from src.explainer import GradCAM, preprocess_image
+
+# Loading Models
+classifier_path = 'models\efficientnet_b1-epoch16-val_loss0.46_ft.ckpt'
+g_NP_checkpoint = 'models\g_NP_best.ckpt'
+g_PN_checkpoint = 'models\g_PN_best.ckpt'
+g_NP = load_model(g_NP_checkpoint, ResUNetGenerator(gf=32, channels=1))
+g_PN = load_model(g_PN_checkpoint, ResUNetGenerator(gf=32, channels=1))
+classifier = load_classifier(classifier_path)
+target_layer = classifier.model.features[-1]
+grad_cam = GradCAM(classifier, target_layer)
+
+
+def counterfactual_generation(input_image):
+
+    translated_images, recon_images = generate_images(input_image, classifier, g_PN, g_NP)
+    translated_images = convert_into_image(translated_images)
+    recon_images = convert_into_image(recon_images)
+    return translated_images, recon_images
+
+def image_classification(input_image):
+
+    result, target_class = classify_image(input_image, classifier=classifier)
+    input_tensor = preprocess_image(input_image)
+    cam = grad_cam.generate_cam(input_tensor, target_class)
+    cam_image = grad_cam.visualize_cam(cam, input_tensor)
+
+    return result, cam_image
+
+# Defining the components
+inputs1 = gr.Image(type="pil", format="png")
+inputs2 = gr.Image(type="pil", format="png")
+outputs1 = [gr.Image(type="pil", label="Translated Images", format="png"),
+           gr.Image(type="pil", label="Reconstructed Images", format="png")]
+
+outputs2 = [gr.Label(label="Classification Result"), gr.Image(label="Grad-CAM", format="png")]
+
+with gr.Blocks() as demo:
+    with gr.Tab("Counterfactual Generation"):
+        app1 = gr.Interface(fn=counterfactual_generation, inputs=inputs1, outputs=outputs1,
+                            title="Counterfactual Image Generation", allow_flagging="never",
+                            description="Generate counterfactual images to explain the classifier's decisions.")
+
+    with gr.Tab("Classification"):
+        app2 = gr.Interface(fn=image_classification, inputs=inputs2, outputs=outputs2,
+                            title="Image Classification", allow_flagging="never",
+                            description="Classify the input medical image and visualize Grad-CAM.")
+
+# Launch the app
 demo.launch(share=True)