requirements.txt

app.py

@@ -272,6 +272,20 @@ def group_words_callback(words: List[str]) -> List[gr.update]:
     print(f"Generated groups: {groups}")
     return [gr.update(value=group.words, choices=group.words, info=group.explanation) for group in groups]
 
+def generate_clues_callback(group):
+    """
+    Callback function to generate a clue for the given words.
+
+    Args:
+        words: a list of words.
+
+    Returns:
+        A gradio update object for the given group input.
+    """
+    print("Generating clues: ", group)
+    g = generate_clue(group)
+    return gr.update(value=g.word, info=g.explanation)
+
 
 if __name__ == '__main__':
     with gr.Blocks() as demo:
@@ -304,24 +318,6 @@ if __name__ == '__main__':
                 buttons.append(clue_button)
                 outputs.append(clue_output)
 
-        def pad_or_truncate(lst, n=4):
-            # Ensure the length of the list is at most n
-            truncated_lst = lst[:n]
-            return truncated_lst + (n - len(truncated_lst)) * [Group(words=[],clue='',explanation='')]
-
-
-        def group_words_callback(words):
-            groups = group_words(words)
-            groups = pad_or_truncate(groups, 4)
-            print("Got groups: ", groups, type(groups))
-            return [gr.update(value=groups[i].words, choices=groups[i].words, info=groups[i].explanation) for i in range(4)]
-
-
-
-        def generate_clues_callback(group):
-            print("Generating clues: ", group)
-            g = generate_clue(group)
-            return gr.update(value=g.word, info=g.explanation)
 
 
         model = models.transformers("microsoft/Phi-3-mini-4k-instruct",

requirements.txt

@@ -0,0 +1 @@
+outlines