Update app.py

app.py

@@ -1,81 +1,77 @@
-# import gradio as gr
-# import tensorflow as tf
-# from PIL import Image
-# import numpy as np
-
-# # Teachable Machineで作成したモデルのパス
-# model_path = "keras_model.h5"
-
-# # モデルの読み込み
-# model = tf.keras.models.load_model(model_path)
-
-# # クラスのラベル（Teachable Machineで指定したクラス名と同じ順序で設定）
-# class_labels = [
-# "Cotton",
-# "Linen",
-# "Silk",
-# "Wool",
-# "Polyester",
-# "Nylon",
-# "Rayon",
-# "Fleece",
-# "Leather",
-# "Synth Leather"
-# ]
-
-# # GradioのUIの設定
-# def classify_image(img):
-#     # 画像の前処理
-#     img = Image.fromarray((img * 255).astype(np.uint8))
-#     img = img.resize((224, 224))
-#     img_array = tf.keras.preprocessing.image.img_to_array(img)
-#     img_array = tf.expand_dims(img_array, 0)  # バッチの次元を追加
-
-#     # モデルの予測
-#     predictions = model.predict(img_array)
-#     predicted_class = class_labels[np.argmax(predictions)]
-
-#     return predicted_class
-
-# # Gradio UIの作成
-# iface = gr.Interface(
-#     fn=classify_image,
-#     inputs=gr.Image(),
-#     outputs=gr.Textbox(),
-#     # live=True,
-# )
-
-# # UIの起動
-# iface.launch(share=True)
-from fastai.vision.all import *
 import gradio as gr
-
-learn = load_learner('export.pkl')
-
-categories = ('Blouse', 'Dress', 'Pants', 'Shirt', 'Shorts')
-title = "Clothing Identifier"
-
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Teachable Machineで作成したモデルのパス
+model_path = "keras_model.h5"
+
+# モデルの読み込み
+model = tf.keras.models.load_model(model_path)
+
+# クラスのラベル（Teachable Machineで指定したクラス名と同じ順序で設定）
+class_labels = [
+"Cotton",
+"Linen",
+"Silk",
+"Wool",
+"Polyester",
+"Nylon",
+"Rayon",
+"Fleece",
+"Leather",
+"Synth Leather"
+]
+
+# GradioのUIの設定
 def classify_image(img):
-    pred, idx, probs = learn.predict(img)
-    highest_prob_index = probs.argmax()  # Find the index of the highest probability
-    return categories[highest_prob_index]  # Return the class with the highest probability
+    # 画像の前処理
+    img = Image.fromarray((img * 255).astype(np.uint8))
+    img = img.resize((224, 224))
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0)  # バッチの次元を追加
 
+    # モデルの予測
+    predictions = model.predict(img_array)
+    predicted_class = class_labels[np.argmax(predictions)]
 
+    return predicted_class
 
+# Gradio UIの作成
 iface = gr.Interface(
     fn=classify_image,
     inputs=gr.Image(),
     outputs=gr.Textbox(),
-    title = title,
-    examples = ['dress.jpg', 'shirt.jpg', 'pants.jpg', 'shorts.jpg'],
-    
     # live=True,
 )
+
+# UIの起動
 iface.launch(share=True)
-# image = gr.Image(shape=(512, 512))
-# label = gr.Label()
-# examples = ['dress.jpg', 'shirt.jpg', 'pants.jpg', 'shorts.jpg']
 
-# intf = gr.Interface(fn=classify_image, title=title, inputs=image, outputs=label, examples=examples)
-# intf.launch(inline=False)
 
+
+# from fastai.vision.all import *
+# import gradio as gr
+
+# learn = load_learner('export.pkl')
+
+# categories = ('Blouse', 'Dress', 'Pants', 'Shirt', 'Shorts')
+# title = "Clothing Identifier"
+
+# def classify_image(img):
+#     pred, idx, probs = learn.predict(img)
+#     highest_prob_index = probs.argmax()  # Find the index of the highest probability
+#     return categories[highest_prob_index]  # Return the class with the highest probability
+
+
+
+# iface = gr.Interface(
+#     fn=classify_image,
+#     inputs=gr.Image(),
+#     outputs=gr.Textbox(),
+#     title = title,
+#     examples = ['dress.jpg', 'shirt.jpg', 'pants.jpg', 'shorts.jpg'],
+    
+#     # live=True,
+# )
+# iface.launch(share=True)