Update views.py

backend/projects/views.py

@@ -878,8 +878,71 @@ def convert_prediction_json_to_annotation_result(pk, proj_type):
         # converting prediction_json to result (wherever it exists) for every task.
         if prediction_json == None:
             return result
-        for pred_type, pred_json in prediction_json.items():
-            for idx, val in enumerate(pred_json):
+        # for pred_type, pred_json in prediction_json.items():
+        if 'acoustic_normalised_transcribed_json' in pred_json.keys():
+            for idx, val, val_acoustic in enumerate(zip(pred_json['verbatim_transcribed_json'],pred_json['acoustic_normalised_transcribed_json'])):
+                label_dict = {
+                    "origin": "manual",
+                    "to_name": "audio_url",
+                    "from_name": "labels",
+                    "original_length": audio_duration,
+                }
+                text_dict = {
+                    "origin": "manual",
+                    "to_name": "audio_url",
+                    "from_name": "transcribed_json",
+                    "original_length": audio_duration,
+                }
+                text_dict_acoustic = {
+                    "origin": "manual",
+                    "to_name": "audio_url",
+                    "from_name": "transcribed_json",
+                    "original_length": audio_duration,
+                }
+                if proj_type == "AcousticNormalisedTranscriptionEditing":
+                    text_dict["from_name"] = 'verbatim_transcribed_json'
+                    text_dict_acoustic["from_name"] = 'acoustic_normalised_transcribed_json'
+
+                id = f"shoonya_{idx}s{generate_random_string(13 - len(str(idx)))}"
+                label_dict["id"] = id
+                text_dict["id"] = id
+                text_dict_acoustic["id"] = id
+
+                label_dict["type"] = "labels"
+                text_dict["type"] = "textarea"
+                text_dict_acoustic["type"] = "textarea"
+
+                value_labels = {
+                    "start": val["start"],
+                    "end": val["end"],
+                    "labels": [
+                        next(
+                            speaker
+                            for speaker in speakers_json
+                            if speaker["speaker_id"] == val["speaker_id"]
+                        )["name"]
+                    ],
+                }
+                value_text = {
+                    "start": val["start"],
+                    "end": val["end"],
+                    "text": [val["text"]],
+                }
+                value_text_acoustic = {
+                    "start": val_acoustic["start"],
+                    "end": val_acoustic["end"],
+                    "text": [val_acoustic["text"]],
+                }
+
+                label_dict["value"] = value_labels
+                text_dict["value"] = value_text
+                text_dict_acoustic["value"] = value_text_acoustic
+                # mainly label_dict and text_dict are sent as result
+                result.append(label_dict)
+                result.append(text_dict)
+                result.append(text_dict_acoustic)
+        else:
+            for idx, val in enumerate(pred_json['verbatim_transcribed_json']):
                 label_dict = {
                     "origin": "manual",
                     "to_name": "audio_url",
@@ -893,7 +956,7 @@ def convert_prediction_json_to_annotation_result(pk, proj_type):
                     "original_length": audio_duration,
                 }
                 if proj_type == "AcousticNormalisedTranscriptionEditing":
-                    text_dict["from_name"] = pred_type
+                    text_dict["from_name"] = 'verbatim_transcribed_json'
                 id = f"shoonya_{idx}s{generate_random_string(13 - len(str(idx)))}"
                 label_dict["id"] = id
                 text_dict["id"] = id