Update app.py

app.py

@@ -6,6 +6,7 @@ import numpy as np
 from PIL import Image
 import tensorflow as tf
 from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
+from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
 
 feature_extractor = SegformerFeatureExtractor.from_pretrained(
     "nvidia/segformer-b0-finetuned-cityscapes-512-1024"
@@ -102,19 +103,12 @@ def sepia(input_img):
     return fig
 
     custom_template = "my_custom_template.html"
-
-def explain_prediction(input_img):
-    # 모델 예측 수행
-    prediction = my_model.predict(input_img)
-
-    # 예측 결과를 설명하는 텍스트 생성
-    explanation = f"The model predicts this image as class {prediction}."
-
-    # 시각화: 예측 설명 텍스트를 이미지에 표시
-    result_image = input_img.copy()
-    cv2.putText(result_image, explanation, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-    return result_image
+model = ResNet50()
+def classify_image(inp):
+    inp = preprocess_input(inp)
+    preds = model.predict(inp)
+    label = decode_predictions(preds, top=1)[0][0][1]
+    return label
 
 
 demo = gr.Interface(fn=sepia,