Update app.py

app.py

@@ -1,46 +1,46 @@
-import tensorflow as tf
-import gradio as gr
-
-from src.load_dataset.load_dataset import classes
-
-nm_model = tf.keras.models.load_model("../models/mn_model.keras")
-
-resnet_model = tf.keras.models.load_model("../models/newmodel.h5")
-
-inception_model = tf.keras.models.load_model("../models/inception_v3.keras")
-
-cifar10_labels = classes
-models = ["ResNetBased Model", "MobileNetBased Model", "InceptionBased Model"]
-
-
-def classify_image(input_image, model_name):
-    try:
-        input_image = tf.image.resize(input_image, (32, 32))
-        labels = cifar10_labels
-        model = get_model(model_name)
-        input_image = tf.expand_dims(input_image, axis=0)
-        predictions = model.predict(input_image).flatten()
-        top_indices = predictions.argsort()[-10:][::-1]
-        confidences = {labels[i]: float(predictions[i]) for i in top_indices}
-        return confidences
-    except Exception as e:
-        return {"error": str(e)}
-
-
-def get_model(model_name):
-    if model_name == "MobileNetBased Model":
-        return nm_model
-    elif model_name == "ResNetBased Model":
-        return resnet_model
-    elif model_name == "InceptionBased Model":
-        return inception_model
-
-
-interface = gr.Interface(
-    fn=classify_image,
-    inputs=[gr.Image(type="numpy", image_mode="RGB", label="Input Image"),
-            gr.Dropdown(models, label="Model Choice")],
-    outputs=gr.Label(num_top_classes=3, label="Predictions"),
-)
-
-interface.launch(debug=False, share=True)
+import tensorflow as tf
+import gradio as gr
+
+from src.load_dataset.load_dataset import classes
+
+nm_model = tf.keras.models.load_model("mn_model.keras")
+
+# resnet_model = tf.keras.models.load_model("../models/newmodel.h5")
+
+inception_model = tf.keras.models.load_model("inception_v3.keras")
+
+cifar10_labels = classes
+models = [ "MobileNetBased Model", "InceptionBased Model"]
+
+
+def classify_image(input_image, model_name):
+    try:
+        input_image = tf.image.resize(input_image, (32, 32))
+        labels = cifar10_labels
+        model = get_model(model_name)
+        input_image = tf.expand_dims(input_image, axis=0)
+        predictions = model.predict(input_image).flatten()
+        top_indices = predictions.argsort()[-10:][::-1]
+        confidences = {labels[i]: float(predictions[i]) for i in top_indices}
+        return confidences
+    except Exception as e:
+        return {"error": str(e)}
+
+
+def get_model(model_name):
+    if model_name == "MobileNetBased Model":
+        return nm_model
+    # elif model_name == "ResNetBased Model":
+    #     return resnet_model
+    elif model_name == "InceptionBased Model":
+        return inception_model
+
+
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=[gr.Image(type="numpy", image_mode="RGB", label="Input Image"),
+            gr.Dropdown(models, label="Model Choice")],
+    outputs=gr.Label(num_top_classes=3, label="Predictions"),
+)
+
+interface.launch(debug=False, share=True)