url: https://colab.research.google.com/drive/1WQEL58JYdqVd3Ruytcm4uQImCN8vFxn6?usp=sharing

import gc

def clear_memory():
"""Clears GPU memory."""
torch.cuda.empty_cache()
gc.collect()

def extract_question_from_image(image_input, device="cuda"):
"""
Extracts the question and options from an image using a multimodal model.
"""
if isinstance(image_input, str):
try:
image = Image.open(image_input).convert("RGB")
except Exception as e:
print(f"Error opening image: {e}")
return "Error: Could not open image."
else:
image = image_input.convert("RGB")

# Resize for efficiency and to avoid OOM errors.  Adjust as needed.
max_size = (512, 512)
image.thumbnail(max_size, Image.LANCZOS)

prompt = "Extract the question from image and diagram and all options from this image."  # Simple, direct prompt

try:
    encoding = processor(
        images=image,
        text=prompt,  # Pass the prompt text here
        return_tensors="pt",
        padding=True,  # Add padding
    ).to(device)

    with torch.no_grad():
        outputs = model.generate(
            **encoding,
            max_new_tokens=1024,  # Adjust as needed
            do_sample=False,  # Use greedy decoding for deterministic output
            temperature=0.1,
            num_beams=1
        )

    # Decode, skipping the prompt tokens
    input_length = encoding.input_ids.shape[1]
    generated_text = tokenizer.decode(
        outputs[0][input_length:], skip_special_tokens=True
    ).strip()


    return generated_text

except Exception as e:
    print(f"Error during generation: {e}")
    clear_memory()
    return "Error: Could not extract question from image."
finally:
    # Clean up, even if there's no error
    if 'encoding' in locals():
        for k in encoding:
            if isinstance(encoding[k], torch.Tensor):
                encoding[k] = encoding[k].cpu()
    if 'outputs' in locals():
         outputs = outputs.cpu()
    clear_memory()

def process_image_sample(sample, device="cuda"):
"""Processes a single image sample from the dataset."""
if "image" not in sample or not sample["image"]:
print("No image found in the sample.")
return None

# Display the image (optional, good for debugging)
display(sample["image"])  # Uncomment if you're in a notebook environment

try:
    extracted_text = extract_question_from_image(sample["image"], device)
    print(f"Extracted Text: {extracted_text}")
    return extracted_text
except Exception as e:
    print(f"Error processing image: {e}")
    return None
finally:
    clear_memory()

--- Example Usage ---

if name == "main":
# Process a few samples (adjust the range as needed)
for i in range(min(5, len(dataset))): # Process up to 5 samples, or fewer if the dataset is smaller
print(f"Processing sample {i+1}:")
process_image_sample(dataset[i])
print("-" * 20)
clear_memory()