Update app.py

app.py

@@ -1,110 +1,32 @@
 import gradio as gr
-from matplotlib import gridspec
-import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
-import tensorflow as tf
-from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
+from tensorflow.keras.models import load_model
 
+# 이미지 분류 모델을 불러옵니다. 여기에서는 간단한 예시 모델을 사용합니다.
+model = load_model('nvidia/segformer-b1-finetuned-cityscapes-1024-1024')  # 모델 경로를 적절하게 수정하세요.
 
-feature_extractor = SegformerFeatureExtractor.from_pretrained(
-    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
-)
-model = TFSegformerForSemanticSegmentation.from_pretrained(
-    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
-)
+# 모델 입력 크기를 확인합니다.
+input_size = model.input_shape[1:3]
 
-def ade_palette():
-    """ADE20K palette that maps each class to RGB values."""
-    return [
-        [204, 166, 62],
-        [188, 229, 92],
-        [47, 157,39],
-        [178, 235, 244],
-        [0, 51, 153],
-        [181, 178, 255],
-        [128, 65, 217],
-        [255, 178, 245],
-        [153, 0, 76],
-        [25, 186, 52],
-        [81, 162, 235],
-        [255, 255, 0],
-        [62, 57, 159],
-        [91, 189, 203],
-        [0, 0, 255],
-        [0, 255, 255],
-        [12, 168, 0],
-        [255, 0, 0],
-        [231, 32, 65]
-    ]
+# 모델 예측 함수를 정의합니다.
+def classify_image(img):
+    # 이미지를 모델 입력 크기에 맞게 조정합니다.
+    img = img.resize(input_size)
+    img_array = np.array(img) / 255.0  # 이미지를 0에서 1 사이로 정규화합니다.
+    img_array = np.expand_dims(img_array, axis=0)  # 배치 차원을 추가합니다.
 
-labels_list = []
+    # 모델로 예측을 수행합니다.
+    predictions = model.predict(img_array)
+    
+    # 예측 결과 중에서 가장 높은 확률을 가진 클래스를 선택합니다.
+    predicted_label = np.argmax(predictions)
 
-with open(r'labels.txt', 'r') as fp:
-    for line in fp:
-        labels_list.append(line[:-1])
+    # 라벨을 반환합니다.
+    return predicted_label
 
-colormap = np.asarray(ade_palette())
+# Gradio UI를 생성합니다.
+iface = gr.Interface(fn=classify_image, inputs="image", outputs="label", live=True)
 
-def label_to_color_image(label):
-    if label.ndim != 2:
-        raise ValueError("Expect 2-D input label")
-
-    if np.max(label) >= len(colormap):
-        raise ValueError("label value too large.")
-    return colormap[label]
-
-def draw_plot(pred_img, seg):
-    fig = plt.figure(figsize=(20, 15))
-
-    grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
-
-    plt.subplot(grid_spec[0])
-    plt.imshow(pred_img)
-    plt.axis('off')
-    LABEL_NAMES = np.asarray(labels_list)
-    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
-    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
-
-    unique_labels = np.unique(seg.numpy().astype("uint8"))
-    ax = plt.subplot(grid_spec[1])
-    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
-    ax.yaxis.tick_right()
-    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
-    plt.xticks([], [])
-    ax.tick_params(width=0.0, labelsize=25)
-    return fig
-
-def sepia(input_img):
-    input_img = Image.fromarray(input_img)
-
-    inputs = feature_extractor(images=input_img, return_tensors="tf")
-    outputs = model(**inputs)
-    logits = outputs.logits
-
-    logits = tf.transpose(logits, [0, 2, 3, 1])
-    logits = tf.image.resize(
-        logits, input_img.size[::-1]
-    )  
-    seg = tf.math.argmax(logits, axis=-1)[0]
-
-    color_seg = np.zeros(
-        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
-    )  
-    for label, color in enumerate(colormap):
-        color_seg[seg.numpy() == label, :] = color
-
-    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
-    pred_img = pred_img.astype(np.uint8)
-
-    fig = draw_plot(pred_img, seg)
-    return fig
-
-demo = gr.Interface(fn=sepia,
-                    inputs=gr.Image(shape=(800, 600)),
-                    outputs=['plot'],
-                    examples=["cityoutdoor-1.jpg", "cityoutdoor-2.jpg", "cityoutdoor-3.jpg"],
-                    allow_flagging='never')
-
-
-demo.launch()
+# Gradio UI를 시작합니다.
+iface.launch()