Update app.py

app.py

@@ -21,6 +21,8 @@ import torch
 from huggingface_hub import InferenceClient
 from transformers import AutoTokenizer
 from transformers import AutoImageProcessor
+from datasets import load_dataset
+import pyarrow
 import spaces 
 IS_SPACES_ZERO = os.environ.get("SPACES_ZERO_GPU", "0") == "1"
 IS_SPACE = os.environ.get("SPACE_ID", None) is not None
@@ -49,9 +51,7 @@ image_processor = AutoImageProcessor.from_pretrained(MODEL_ID)
 # Load models only once
 processor = AutoProcessor.from_pretrained(MODEL_ID)
 model = LlavaForConditionalGeneration.from_pretrained(MODEL_ID, quantization_config=quantization_config, device_map="auto")
-# Pass the tokenizer explicitly to the pipeline
-# Pass the image processor explicitly to the pipeline
-#pipe_image_to_text = pipeline("image-to-text", model=model, tokenizer=tokenizer_image_to_text, image_processor=image_processor, model_kwargs={"quantization_config": quantization_config})
+# Pass the tokenizer, image processor explicitly to the pipeline
 # Initialize the text generation pipeline
 
 pipe_text = pipeline(