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app.py

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+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)

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:51868c5813592fbe0e31a63067d18b78852869c7f31bfcaf5c9b0443cd8a7e2c
+size 2458608

labels.txt

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+0 綿(コットン Cotton)
+1 麻(リネン Linen)
+2 絹(シルク Silk)
+3 毛(ウール Wool)
+4 ポリエステル(Polyester)
+5 ナイロン(Nylon)
+6 レーヨン(Rayon)
+7 フリース(Fleece)
+8 革(レザー Leather)
+9 合皮(フェイクレザー Synthetic leather)

machine.py

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+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
+
+# Disable scientific notation for clarity
+np.set_printoptions(suppress=True)
+
+# Load the model
+model = load_model("keras_model.h5", compile=False)
+
+# Load the labels
+class_names = open("labels.txt", "r").readlines()
+
+# Create the array of the right shape to feed into the keras model
+# The 'length' or number of images you can put into the array is
+# determined by the first position in the shape tuple, in this case 1
+data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+
+image = Image.open("216541_0.jpg").convert("RGB")
+
+# resizing the image to be at least 224x224 and then cropping from the center
+size = (224, 224)
+image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
+
+# turn the image into a numpy array
+image_array = np.asarray(image)
+
+# Normalize the image
+normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+
+# Load the image into the array
+data[0] = normalized_image_array
+
+# Predicts the model
+prediction = model.predict(data)
+index = np.argmax(prediction)
+class_name = class_names[index]
+confidence_score = prediction[0][index]
+
+# Print prediction and confidence score
+print("Class:", class_name[2:], end="")
+print("Confidence Score:", confidence_score)