Update app.py
Browse files
app.py
CHANGED
@@ -8,9 +8,13 @@ import time
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import json
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import numpy as np
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import cv2
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from inference_sdk import InferenceHTTPClient
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import matplotlib.pyplot as plt
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import base64
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# Load model and tokenizer
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@st.cache_resource
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@@ -47,7 +51,7 @@ if api_key:
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# 이미지를 base64로 인코딩
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encoded_image = base64.b64encode(image_data).decode('utf-8')
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# 원본 이미지 로드
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image = cv2.imread(image_path)
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image = cv2.resize(image, (800, 600))
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mask = np.zeros(image.shape, dtype=np.uint8)
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@@ -55,9 +59,8 @@ if api_key:
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try:
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# Roboflow API 호출
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results = CLIENT.infer(encoded_image, model_id="closet/1")
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except
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st.error(f"API Error: {api_error
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st.error(f"API Message: {api_error.api_message}")
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return Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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if 'predictions' in results:
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import json
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import numpy as np
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import cv2
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from inference_sdk import InferenceHTTPClient
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import matplotlib.pyplot as plt
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import base64
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# 오류 처리를 위한 예외 클래스 정의
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class APIError(Exception):
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pass
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# Load model and tokenizer
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@st.cache_resource
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# 이미지를 base64로 인코딩
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encoded_image = base64.b64encode(image_data).decode('utf-8')
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# 원본 이미지 로드
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image = cv2.imread(image_path)
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image = cv2.resize(image, (800, 600))
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mask = np.zeros(image.shape, dtype=np.uint8)
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try:
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# Roboflow API 호출
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results = CLIENT.infer(encoded_image, model_id="closet/1")
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except Exception as api_error:
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st.error(f"API Error: {str(api_error)}")
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return Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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if 'predictions' in results:
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