Add finetuned models

roboflow/__init__.py

@@ -13,7 +13,7 @@
 from roboflow.models import CLIPModel, GazeModel
 from roboflow.util.general import write_line
 
-__version__ = "1.1.8"
+__version__ = "1.1.9"
 
 
 def check_key(api_key, model, notebook, num_retries=0):

roboflow/models/inference.py

@@ -239,7 +239,6 @@ def predict_video(
             signed_url = video_path
 
         url = urljoin(API_URL, "/videoinfer/?api_key=" + self.__api_key)
-
         if model_class in ("CLIPModel", "GazeModel"):
             if model_class == "CLIPModel":
                 model = "clip"
@@ -257,6 +256,14 @@ def predict_video(
                     ],
                 }
             ]
+        else:
+            models = [
+                {
+                    "model_id": self.dataset_id,
+                    "model_version": self.version,
+                    "inference_type": self.type,
+                }
+            ]
 
         for model in additional_models:
             models.append(SUPPORTED_ADDITIONAL_MODELS[model])
@@ -308,28 +315,28 @@ def poll_for_video_results(self, job_id: str = None) -> dict:
         url = urljoin(
             API_URL, "/videoinfer/?api_key=" + self.__api_key + "&job_id=" + self.job_id
         )
-
         try:
             response = requests.get(url, headers={"Content-Type": "application/json"})
         except Exception as e:
             raise Exception(f"Error getting video inference results: {e}")
 
         if not response.ok:
             raise Exception(f"Error getting video inference results: {response.text}")
-
         data = response.json()
+        if "status" not in data:
+            return {}  # No status available
+        if data.get("status") > 1:
+            return data  # Error
+        elif data.get("status") == 1:
+            return {}  # Still running
+        else:  # done
+            output_signed_url = data["output_signed_url"]
+            inference_data = requests.get(
+                output_signed_url, headers={"Content-Type": "application/json"}
+            )
 
-        if data.get("status") != 0:
-            return {}
-
-        output_signed_url = data["output_signed_url"]
-
-        inference_data = requests.get(
-            output_signed_url, headers={"Content-Type": "application/json"}
-        )
-
-        # frame_offset and model name are top-level keys
-        return inference_data.json()
+            # frame_offset and model name are top-level keys
+            return inference_data.json()
 
     def poll_until_video_results(self, job_id) -> dict:
         """
@@ -357,14 +364,11 @@ def poll_until_video_results(self, job_id) -> dict:
             job_id = self.job_id
 
         attempts = 0
-
+        print(f"Checking for video inference results for job {job_id} every 60s")
         while True:
+            time.sleep(60)
             print(f"({attempts * 60}s): Checking for inference results")
-
             response = self.poll_for_video_results()
-
-            time.sleep(60)
-
             attempts += 1
 
             if response != {}: