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Update app.py
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app.py
CHANGED
@@ -36,11 +36,13 @@ def predict_fingerprint(image):
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fingerprint_type = get_fingerprint_type(winner)
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return fingerprint_type
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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def preprocess_image(image):
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# Guardar la imagen en formato TIFF
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image.save("temp_image.tif")
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# Aplicar el mismo preprocesamiento que a arco1.tif
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processed_image = representativo("temp_image.tif")
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# Redimensionar la imagen procesada para que coincida con las dimensiones esperadas por el modelo SOM
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@@ -57,12 +59,14 @@ def get_fingerprint_type(winner):
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async def predict_fingerprint_api(file: UploadFile = File(...)):
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try:
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contents = await file.read()
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print(f"
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image = Image.open(BytesIO(contents))
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print(f"
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fingerprint_type = predict_fingerprint(image)
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return {"prediction": fingerprint_type}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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def sobel(I):
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fingerprint_type = get_fingerprint_type(winner)
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return fingerprint_type
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except Exception as e:
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print("prediccion")
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raise HTTPException(status_code=500, detail=str(e))
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def preprocess_image(image):
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# Guardar la imagen en formato TIFF
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image.save("temp_image.tif")
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print(f"se guard贸{image}")
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# Aplicar el mismo preprocesamiento que a arco1.tif
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processed_image = representativo("temp_image.tif")
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# Redimensionar la imagen procesada para que coincida con las dimensiones esperadas por el modelo SOM
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async def predict_fingerprint_api(file: UploadFile = File(...)):
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try:
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contents = await file.read()
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print(f"contenido:\n{contents}")
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image = Image.open(BytesIO(contents))
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print(f"imagen:\n{image}")
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fingerprint_type = predict_fingerprint(image)
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return {"prediction": fingerprint_type}
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except Exception as e:
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print("api")
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raise HTTPException(status_code=500, detail=str(e))
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def sobel(I):
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