WEBVTT

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In this session. we will implement an API to retrieve fire detection logs with pagination. 

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First. we will create the `get_detection_log` domain and add `router`. `schema`. and `crud` files within it.  

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The `router` file assembles the service logic and sequentially calls the contents of `crud` and `schema`.  

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When calling this API. parameters for pagination are required.  

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`page` – The page number to retrieve .

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The number of records to display per page  

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A filter to retrieve logs that meet specific conditions.

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Now. let’s take a look at the `router` file.  

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The received parameters are passed to the `get_detection_logs` function in `crud`. which retrieves the log data. 

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The `get_total_detection_logs_count` function is called to calculate the total number of pages.  

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Finally. the retrieved log list and total count are returned.  

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Looking at the `crud` file. the overall flow is as follows.

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A query object is created to search for data in the `DetectionLog` table  

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So that conditions. sorting. and pagination can be applied later  

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Allowing for efficient retrieval of the desired data. 

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Sorting is done in descending order based on `created_at`. 

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offset is used for pagination.  

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limit is set to restrict the number of records per page.  

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The `detections` field in the `DetectionLog` object retrieved from the database is stored as a JSON string.  

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We need to convert this string into a Python object. 
To make it easier to work with in subsequent code. 

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Additionally. the `get_total_detection_logs_count` function simply retrieves the total number of logs.  

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Finally. the processed logs are returned.  

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The `schema` file defines the return type of the API.  

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Additionally. since the `DetectionLog` model needs to be shared.  

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It is defined in the share schema file.  

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With all the files created. we will now proceed with testing.  

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We use the SQLite Extension to select the database and check the log data.  

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The fire detection API is executed to insert test data in advance.  

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Next. we will test the API using Postman.  

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We verify the pagination functionality by adjusting the `page` and `page_size` parameters.  

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This concludes the FastAPI implementation.  

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In the next session. we will build the frontend using Next.js.