Update app.py

app.py

@@ -5,11 +5,15 @@ import gradio as gr
 from PIL import Image
 import torch
 from transformers import BlipProcessor, BlipForQuestionAnswering
+from concurrent.futures import ThreadPoolExecutor
 
 # Initialize the model and processor
 processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
 model = BlipForQuestionAnswering.from_pretrained("ManishThota/InstructBlip-VQA")
 
+executor = ThreadPoolExecutor(max_workers=4)
+
+
 def predict_answer(image, question):
     # Convert PIL image to RGB if not already
     image = image.convert("RGB")
@@ -17,15 +21,20 @@ def predict_answer(image, question):
     # Prepare inputs
     encoding = processor(image, question, return_tensors="pt")
 
-    out = model.generate(**encoding, max_length=512)
+    out = model.generate(**encoding)
     generated_text = processor.decode(out[0], skip_special_tokens=True)
 
     return generated_text
 
 
+# def gradio_predict(image, question):
+#     answer = predict_answer(image, question)
+#     return answer
+
 def gradio_predict(image, question):
-    answer = predict_answer(image, question)
-    return answer
+    future = executor.submit(predict_answer, image, question)
+    return future.result()
+
 
 # Define the Gradio interface
 iface = gr.Interface(