Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-
+import os
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -11,12 +11,64 @@ from tensorflow.keras.utils import to_categorical
 from tensorflow.keras.applications.resnet_v2 import preprocess_input
 from tensorflow.keras.preprocessing.image import load_img, img_to_array
 
+image_folders = ['King_Crab', 'Wind_Lion_God', 'pavo_cristatus', 'otter', 'Upupa_epops']
+labels = ["鱟", "金門風獅爺", "金門藍孔雀", "歐亞水獺", "金門戴勝鳥"]
+
+base_dir = './'
+
+thedir = base_dir + image_folders[0]
+os.listdir(thedir)
+
+data = []
+target = []
+for i in range(5):
+    thedir = base_dir + image_folders[i]
+    image_fnames = os.listdir(thedir)
+    for theimage in image_fnames:
+        if theimage == ".git" or theimage == ".ipynb_checkpoints":
+          continue
+        img_path = thedir + '/' + theimage
+        img = load_img(img_path , target_size = (256,256))
+        x = img_to_array(img)
+        data.append(x)
+        target.append(i)
+
+model = load_model('my_cnn_model.pb') # Loading the Tensorflow Saved Model (PB)
+print(model.summary())
+
+def classify_image(inp):
+    inp = inp.reshape((-1, 256, 256, 3))
+    inp = preprocess_input(inp)
+    prediction = model.predict(inp).flatten()
+    return {labels[i]: float(prediction[i]) for i in range(5)}
 
-def greet(name):
+image = gr.Image(shape=(256, 256), label="金門藍孔雀、歐亞水獺、戴勝鳥照片")
+label = gr.Label(num_top_classes=5, label="AI辨識結果")
+some_text="我能辨識金門藍孔雀、歐亞水獺、戴勝鳥。找張金門藍孔雀、歐亞水獺、戴勝鳥照片來考我吧!"
 
-    model = load_model('my_cnn_model.h5') # Loading the Tensorflow Saved Model (PB)    
+# sample_images = []
+# for i in range(5):
+#     thedir = base_dir + image_folders[i]
+#     for file in os.listdir(thedir):
+#         if file == ".git" or file == ".ipynb_checkpoints":
+#           continue
+#         sample_images.append(image_folders[i] + '/' + file)
+
+iface = gr.Interface(fn=classify_image,
+             inputs=image,
+             outputs=label,
+             title="AI 金門藍孔雀、歐亞水獺、戴勝鳥辨識機",
+             description=some_text,
+             examples=sample_images, live=True)
+
+# def greet(name):
+
+#     model = load_model('my_cnn_model.h5') # Loading the Tensorflow Saved Model (PB)    
     
-    return "Hello " + name + "!!" + model.summary()
+#     return "Hello " + name + "!!" + model.summary()
+
+
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# .launch(share=True)
 iface.launch()