Update app.py

app.py

@@ -1,38 +1,34 @@
 import gradio as gr
-import cv2
-import numpy as np
 import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
 
-# Teachable MachineモデルのURL
-URL = "https://teachablemachine.withgoogle.com/models/ZPfAhDYCh/"
-
-# モデルとメタデータのURL
-model_url = URL + "model.json"
-metadata_url = URL + "metadata.json"
-
-# モデルとメタデータのロード
-model = tf.keras.models.load_model(model_url)
-metadata = gr.tfjs.ModelMetadata(metadata_url)
-
-# ウェブカメラのキャプチャ
-cap = cv2.VideoCapture(0)
+# Load the Teachable Machine Image Model
+model_url = "https://teachablemachine.withgoogle.com/models/ZPfAhDYCh/"
+model = tf.keras.models.load_model(model_url + "model.json")
 
-# カメラからの入力を処理する関数
-def classify_image(frame):
-    # フレームをリサイズしてモデルに適した形式に変換
-    input_data = cv2.resize(frame, (200, 200))
-    input_data = np.expand_dims(input_data, axis=0)
+# Function to preprocess the image before making predictions
+def preprocess_image(img):
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img /= 255.0  # Normalize pixel values to be between 0 and 1
+    return img
 
-    # 画像の予測
-    predictions = model.predict(input_data)
+# Function to make predictions using the loaded model
+def predict_image(img):
+    img = preprocess_image(img)
+    prediction = model.predict(img)
+    return {class_name: float(prediction[0][i]) for i, class_name in enumerate(classes)}
 
-    # 予測結果を表示
-    results = {metadata.get_class_label(i): float(predictions[0][i]) for i in range(len(predictions[0]))}
-    return results
+# Fetch class labels from metadata.json
+metadata_url = model_url + "metadata.json"
+metadata = tf.keras.utils.get_file("metadata.json", metadata_url)
+classes = tf.keras.utils.get_file("classes.txt", metadata_url.replace("metadata.json", "classes.txt")).read().splitlines()
 
-# インターフェースの作成
-iface = gr.Interface(fn=classify_image, inputs="webcam", outputs="label")
+# Create Gradio interface
+iface = gr.Interface(fn=predict_image, inputs="image", outputs="label", live=True, capture_session=True)
 
-# インターフェースの起動
+# Launch Gradio interface
 iface.launch()
 
+