handler.py

import torch
from transformers import LlamaForCausalLM, AutoProcessor
from PIL import Image
import base64
import io

# Load model and processor globally
model_id = "kiddobellamy/Llama_Vision"

model = LlamaForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)

def handler(event, context):
    try:
        # Parse inputs
        inputs = event.get('inputs', {})
        image_base64 = inputs.get('image')
        prompt = inputs.get('prompt', '')

        if not image_base64 or not prompt:
            return {'error': 'Both "image" and "prompt" are required in inputs.'}

        # Decode the base64 image
        image_bytes = base64.b64decode(image_base64)
        image = Image.open(io.BytesIO(image_bytes)).convert('RGB')

        # Prepare the message
        messages = [
            {"role": "user", "content": [
                {"type": "image"},
                {"type": "text", "text": prompt}
            ]}
        ]
        input_text = processor.apply_chat_template(messages, add_generation_prompt=True)

        # Process inputs
        inputs = processor(image, input_text, return_tensors="pt").to(model.device)

        # Generate output
        output_ids = model.generate(**inputs, max_new_tokens=50)
        generated_text = processor.decode(output_ids[0], skip_special_tokens=True)

        # Return the result
        return {'generated_text': generated_text}

    except Exception as e:
        return {'error': str(e)}