Delete app.py

app.py

@@ -1,136 +0,0 @@
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-from PIL import Image
-from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
-import matplotlib.pyplot as plt
-from matplotlib import gridspec
-
-feature_extractor = SegformerFeatureExtractor.from_pretrained(
-    "nvidia/segformer-b0-finetuned-cityscapes-1024-1024"
-)
-model = TFSegformerForSemanticSegmentation.from_pretrained(
-    "nvidia/segformer-b0-finetuned-cityscapes-1024-1024"
-)
-
-def ade_palette():
-    """ADE20K palette that maps each class to RGB values."""
-    return [
-        [255, 0, 0],
-        [255, 187, 0],
-        [255, 228, 0],
-        [29, 219, 22],
-        [178, 204, 255],
-        [1, 0, 255], 
-        [165, 102, 255],
-        [217, 65, 197],
-        [116, 116, 116],
-        [204, 114, 61],
-        [206, 242, 121],
-        [61, 183, 204],
-        [94, 94, 94],
-        [196, 183, 59],
-        [246, 246, 246],
-        [209, 178, 255],
-        [0, 87, 102],
-        [153, 0, 76],
-        [47, 157, 39]
-    ]
-
-labels_list = []
-
-with open(r'labels.txt', 'r') as fp:
-    for line in fp:
-        labels_list.append(line[:-1])
-
-colormap = np.asarray(ade_palette())
-
-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]
-
-    probabilities = tf.nn.softmax(logits, axis=-1)[0]  # 확률값 추출
-
-    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)
-
-    # 각 클래스의 확률을 출력
-    class_probabilities = {
-        labels_list[i]: probabilities[:, :, i].numpy() for i in range(len(labels_list))
-    }
-    print(class_probabilities)
-
-    class_probabilities = {
-        labels_list[i]: probabilities[:, :, i].numpy() for i in range(len(labels_list))
-    }
-
-    # 각 클래스의 확률을 출력
-    for label, prob_map in class_probabilities.items():
-        print(f"{label} probabilities:")
-        print(prob_map)
-    
-     class_probabilities = {
-        labels_list[i]: np.max(probabilities[:, :, i].numpy()) for i in range(len(labels_list))
-    }
-
-    # 가장 높은 확률을 가진 클래스를 출력
-    max_prob_class = max(class_probabilities, key=class_probabilities.get)
-    max_prob_value = class_probabilities[max_prob_class]
-    
-    print(f"Predicted class: {max_prob_class}, Probability: {max_prob_value:.4f}")
-
-    return fig, f"Predicted class: {max_prob_class}, Probability: {max_prob_value:.4f}"
-
-demo = gr.Interface(fn=sepia,
-                    inputs=gr.Image(shape=(400, 600)),
-                    outputs=["plot", "text"],
-                    examples=["citiscapes-1.jpeg", "citiscapes-2.jpeg"],
-                    allow_flagging='never')
-
-demo.launch()