Requirement:
- Python 3.5
Python Dependancies:
-
scikit-learn 0.19.0
-
numpy 1.22.1
-
build_models.py ==> builds pretrained models, data dictionary and saves them in pickle format
-
utils.py ==> has timeit method for function call time, elbow method for k-means clustering
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server.py ==> Flask API server serving HTTP POST requests
POST REQUEST
-
Example query:
- {"item_id": "131971", "query_type": "diverse"} in json format
- query_type == "similar" for similar results
- query_type == "diverse" for similar but diverse results
Using python requests
r = requests.post("http://127.0.0.1:5000/predict", headers={'Content-Type': 'application/json'}, data=json.dumps({"item_id": "131971", "query_type": "diverse"}))
print(r.content, r.status_code, r.reason)
b'"{\\"diverse\\": \\"[460802, 30901, 176849, 157305, 452034, 244281, 269178, 207396, 96166, 243109]\\", \\"item_id\\": \\"131971\\"}"\n' 200 OK
Using Postman Chrome extension
- Enter POST request URL
- Choose Body --> application/json --> raw --> Enter example query --> Send