Toy example request and local server response working

http_submission/model_server.py

@@ -33,7 +33,7 @@ def predict():
 
 
 def main():
-    app.run(host="0.0.0.0", port="4000", debug="True", threaded=True)
+    app.run(host="localhost", port="4000", debug="True", threaded=True)
 
 
 if __name__ == "__main__":

http_submission/sample_evaluation.py

@@ -1,10 +1,12 @@
 import numpy as np
 import pickle
-import pysaliency
 import requests
+import sys
+from sample_submission import SampleScanpathModel
+sys.path.insert(0, '..')
+import pysaliency
 
-
-class HTTPScanpathModel(pysaliency.ScanpathModel):
+class HTTPScanpathModel(SampleScanpathModel):
     def __init__(self, url):
         self.url = url
 
@@ -24,6 +26,38 @@ def conditional_log_density(
         # print(f"Received: {response.json()}")
         return np.array(response.json())
 
+# class HTTPScanpathModel(pysaliency.ScanpathModel):
+#     def __init__(self, url):
+#         self.url = url
+
+#     def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None):
+#         # build request
+#         pil_image = Image.fromarray(stimulus)
+#         image_bytes = BytesIO()
+#         pil_image.save(image_bytes, format='png')
+
+#         def _convert_attribute(attribute):
+#             if isinstance(attribute, np.ndarray):
+#                 return attribute.tolist()
+#             return attribute
+
+#         json_data = {
+#             "x_hist": list(x_hist),
+#             "y_hist": list(y_hist),
+#             "t_hist": list(t_hist),
+#             "attributes": {key: _convert_attribute(value) for key, value in (attributes or {}).items()}
+#         }
+
+#         # send request
+
+#         response = requests.post(f"{self.url}/conditional_log_density", json=json_data, files={'stimulus': image_bytes.getvalue()})
+
+#         # parse response
+
+#         if response.status_code != 200:
+#             raise ValueError(f"Server returned status code {response.status_code}")
+
+#         return np.array(response.json()['log_density'])
 
 if __name__ == "__main__":
     http_model = HTTPScanpathModel("http://localhost:4000/predict")

http_submission/sample_submission.py

@@ -6,4 +6,45 @@ def __init__(self):
         super().__init__()
 
     def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None):
-        return np.log(stimulus)
+        return np.log(stimulus)
+
+# from io import BytesIO
+
+# import pysaliency
+# import requests
+# from PIL import Image
+# import numpy as np
+
+
+# class HTTPScanpathModel(pysaliency.ScanpathModel):
+#     def __init__(self, url):
+#         self.url = url
+
+#     def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None):
+#         # build request
+#         pil_image = Image.fromarray(stimulus)
+#         image_bytes = BytesIO()
+#         pil_image.save(image_bytes, format='png')
+
+#         def _convert_attribute(attribute):
+#             if isinstance(attribute, np.ndarray):
+#                 return attribute.tolist()
+#             return attribute
+
+#         json_data = {
+#             "x_hist": list(x_hist),
+#             "y_hist": list(y_hist),
+#             "t_hist": list(t_hist),
+#             "attributes": {key: _convert_attribute(value) for key, value in (attributes or {}).items()}
+#         }
+
+#         # send request
+
+#         response = requests.post(f"{self.url}/conditional_log_density", json=json_data, files={'stimulus': image_bytes.getvalue()})
+
+#         # parse response
+
+#         if response.status_code != 200:
+#             raise ValueError(f"Server returned status code {response.status_code}")
+
+#         return np.array(response.json()['log_density'])