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app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import os
-from gradio_interfaces.conversation_labeling.interface_utils import *
+from interface_utils import *
 
 maxim = 'quantity'
 submaxims = ["The response provides a sufficient amount of information.",

interface_utils.py

@@ -0,0 +1,50 @@
+import json
+import numpy as np
+import random
+import uuid
+
+
+def load_from_jsonl(filename, n=np.inf):
+    data = []
+    with open(filename, 'r') as file:
+        for i, line in enumerate(file):
+            if i >= n:  # stop after reading n lines
+                break
+            data.append(json.loads(line))
+    return data
+
+
+def append_id(conversations_no_id):
+    conversations = []
+    for conversation in conversations_no_id:
+        conversations.append({
+            'conv_id': uuid.uuid4().hex,
+            'transcript': conversation['transcript']
+        })
+    return conversations
+
+
+def save_to_jsonl(data, filename):
+    with open(filename, 'w') as file:
+        for item in data:
+            json_line = json.dumps(item)
+            file.write(json_line + '\n')
+
+
+def get_conversation(conversation_data):
+    conv = random.choice(conversation_data)
+    return conv
+
+
+def pad_transcript(transcript, max_length):
+    padding_count = max_length - len(transcript)
+    if padding_count > 0:
+        for _ in range(padding_count):
+            transcript.append({'speaker': '', 'response': ''})
+    return transcript
+
+
+def get_last_response(transcript):
+    for turn in reversed(transcript):
+        if turn['speaker'] and turn['response']:
+            return turn['response']