Update src/model.py

src/model.py

@@ -1,5 +1,4 @@
 # Importing the requirements
-import uuid
 import torch
 from transformers import AutoModel, AutoTokenizer
 import spaces
@@ -23,24 +22,6 @@ tokenizer = AutoTokenizer.from_pretrained(
 model.eval()
 
 
-class _GeneratorPickleHack:
-    def __init__(self, generator, generator_id=None):
-        self.generator = generator
-        self.generator_id = (
-            generator_id if generator_id is not None else str(uuid.uuid4())
-        )
-
-    def __call__(self, *args, **kwargs):
-        return self.generator(*args, **kwargs)
-
-    def __reduce__(self):
-        return (_GeneratorPickleHack_raise, (self.generator_id,))
-
-
-def _GeneratorPickleHack_raise(*args, **kwargs):
-    raise AssertionError("cannot actually unpickle _GeneratorPickleHack!")
-
-
 @spaces.GPU()
 def describe_video(video, question):
     """
@@ -54,9 +35,7 @@ def describe_video(video, question):
         str: The generated answer to the question.
     """
     # Encode the video frames
-    frames = _GeneratorPickleHack(encode_video)(video)
-    #frames = encode_video(video)
-    #frames = list(frames)  # Convert generator or any iterable to list
+    frames = encode_video(video)
 
     # Message format for the model
     msgs = [{"role": "user", "content": frames + [question]}]
@@ -79,5 +58,8 @@ def describe_video(video, question):
         **params
     )
 
-    # Return the answer
-    return answer
+    # Consume the generator and concatenate the results
+    full_answer = "".join(answer)
+
+    # Return the full answer as a string
+    return full_answer