Update app.py

app.py

@@ -2,14 +2,18 @@ import gradio as gr
 import tensorflow as tf
 from PIL import Image
 import numpy as np
+from fastai.vision.all import *
 
-# Teachable Machineで作成したモデルのパス
+
+learn = load_learner('export.pkl')
 model_path = "keras_model.h5"
 
-# モデルの読み込み
+
 model = tf.keras.models.load_model(model_path)
+categories = ('Blouse', 'Dress', 'Pants', 'Shirt', 'Shorts')
+
+title = "Clothing Identifier"
 
-# 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",
@@ -23,29 +27,32 @@ class_labels = [
 "Synth Leather"
 ]
 
-# GradioのUIの設定
+
 def classify_image(img):
-    # 画像の前処理
+    pred, idx, probs = learn.predict(img)
+    highest_prob_index = probs.argmax()
     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
+    img_array = tf.expand_dims(img_array, 0)  
 
-    # モデルの予測
+    
     predictions = model.predict(img_array)
     predicted_class = class_labels[np.argmax(predictions)]
 
-    return predicted_class
+    return The cloth belongs to category categories[highest_prob_index] a predicted_class material
+
 
-# 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)