Create model.py

model.py

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+# Importing the requirements
+from transformers import BlipProcessor, BlipForQuestionAnswering
+
+# Load the model and processor
+processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
+model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
+
+
+# Function to answer the question
+def answer_question(image, text):
+    """
+    Generates an answer to a given question based on the provided image and text.
+
+    Args:
+        image (str): The path to the image file.
+        text (str): The question text.
+
+    Returns:
+        str: The generated answer to the question.
+    """
+
+    # Process the inputs and generate the ids
+    inputs = processor(images=image, text=text, return_tensors="pt")
+    generated_ids = model.generate(**inputs, max_length=50)
+
+    # Decode the generated IDs
+    generated_answer = processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+    # Return the generated answer
+    return generated_answer[0]