Update app.py

app.py

@@ -1,14 +1,16 @@
-from keras.models import load_model
-from PIL import Image, ImageOps
+from keras.models import load_model  # TensorFlow is required for Keras to work
+from PIL import Image, ImageOps  # Install pillow instead of PIL
 import numpy as np
+
 import gradio as gr
-import pyttsx3
+import numpy as np
+# from PIL import Image, ImageOps
 
-# モデルの読み込み
+# Load the model
 model = load_model("keras_model.h5", compile=False)
 
-# ラベルの読み込み
-class_names = open("labels.txt", "r", encoding="utf-8").readlines()
+# Load the labels
+class_names = open("labels.txt", "r",encoding="utf-8").readlines()
 
 def greet(img):
     data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
@@ -23,21 +25,11 @@ def greet(img):
 
     prediction = model.predict(data)
 
-    max_index = np.argmax(prediction)
-    class_name = class_names[max_index]
-
-    # ファイルの内容からクラス名を抽出
-    class_name = class_name[2:]
-
-    # 予測されたクラス名を読み上げる
-    speak_text(class_name)
+    max_index = np.argmax(prediction) # 確率が一番高いインデクスを抽出
 
-    return class_name
+    class_name = class_names[max_index]
 
-def speak_text(text):
-    engine = pyttsx3.init()
-    engine.say(text)
-    engine.runAndWait()
+    return class_name[2:]
 
 demo = gr.Interface(
     fn=greet,
@@ -45,4 +37,5 @@ demo = gr.Interface(
     outputs="text",
 )
 
-demo.launch()
+# demo.launch(debug=True, share=True)
+demo.launch()