Update app.py

Sad update to depressed

app.py

@@ -1,44 +1,44 @@
-import gradio as gr
-import os
-import numpy as np
-import tensorflow as tf
-import keras
-import keras_cv
-from keras.models import load_model
-import cv2
-
-
-def image_predict(img_):
-    model = load_model('efficientnet_b0.keras')
-    img = cv2.cvtColor(img_, cv2.COLOR_BGR2RGB)
-    img = cv2.resize(img, dsize = [224, 224])
-    img = img / 255.0
-    img = np.expand_dims(img, axis = 0)
-
-    pred = model.predict(img, verbose = 1)
-    pred = np.argmax(pred, axis = 1)
-
-    classes = ['angry', 'happy', 'neutral', 'sad', 'suprised', 'tired']
-    if pred == 0:
-        answer = f"Facial Expression detected is: {classes[0].capitalize()}"
-    elif pred == 1:
-        answer = f"Facial Expression detected is: {classes[1].capitalize()}"
-    elif pred == 2:
-        answer = f"Facial Expression detected is: {classes[2].capitalize()}"
-    elif pred == 3:
-        answer = f"Facial Expression detected is: {classes[3].capitalize()}"
-    elif pred == 4:
-        answer = f"Facial Expression detected is: {classes[4].capitalize()}"
-    elif pred == 5:
-        answer = f"Facial Expression detected is: {classes[5].capitalize()}"
-
-    return answer
-
-
-with gr.Blocks() as demo:
-    image_ = gr.Image(label = 'Input Image to be predicted')
-    output = gr.Textbox(label = 'Prediction')
-    btn = gr.Button('Predict')
-    btn.click(fn = image_predict, inputs = [image_], outputs = output)
-
+import gradio as gr
+import os
+import numpy as np
+import tensorflow as tf
+import keras
+import keras_cv
+from keras.models import load_model
+import cv2
+
+
+def image_predict(img_):
+    model = load_model('efficientnet_b0.keras')
+    img = cv2.cvtColor(img_, cv2.COLOR_BGR2RGB)
+    img = cv2.resize(img, dsize = [224, 224])
+    img = img / 255.0
+    img = np.expand_dims(img, axis = 0)
+
+    pred = model.predict(img, verbose = 1)
+    pred = np.argmax(pred, axis = 1)
+
+    classes = ['angry', 'happy', 'neutral', 'sad', 'suprised', 'tired']
+    if pred == 0:
+        answer = f"Facial Expression detected is: {classes[0].capitalize()}"
+    elif pred == 1:
+        answer = f"Facial Expression detected is: {classes[1].capitalize()}"
+    elif pred == 2:
+        answer = f"Facial Expression detected is: {classes[2].capitalize()}"
+    elif pred == 3:
+        answer = f"Facial Expression detected is: Depressed"
+    elif pred == 4:
+        answer = f"Facial Expression detected is: {classes[4].capitalize()}"
+    elif pred == 5:
+        answer = f"Facial Expression detected is: {classes[5].capitalize()}"
+
+    return answer
+
+
+with gr.Blocks() as demo:
+    image_ = gr.Image(label = 'Input Image to be predicted')
+    output = gr.Textbox(label = 'Prediction')
+    btn = gr.Button('Predict')
+    btn.click(fn = image_predict, inputs = [image_], outputs = output)
+
 demo.launch(share = False)