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import requests
import torch
from PIL import Image
import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor
# Load model and processor
model_id_or_path = "rhymes-ai/Aria"
model = AutoModelForCausalLM.from_pretrained(model_id_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True)
# Function to process the input and generate text
def generate_response(image):
# Convert the input image to PIL format (if necessary)
if isinstance(image, str):
image = Image.open(requests.get(image, stream=True).raw)
# Prepare messages for the model
messages = [
{
"role": "user",
"content": [
{"text": None, "type": "image"},
{"text": "what is the image?", "type": "text"},
],
}
]
text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=text, images=image, return_tensors="pt")
# Move pixel values to the correct dtype
inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
# Generate response
with torch.inference_mode(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
output = model.generate(
**inputs,
max_new_tokens=500,
stop_strings=["<|im_end|>"],
tokenizer=processor.tokenizer,
do_sample=True,
temperature=0.9,
)
output_ids = output[0][inputs["input_ids"].shape[1]:]
result = processor.decode(output_ids, skip_special_tokens=True)
return result
# Gradio interface
iface = gr.Interface(
fn=generate_response,
inputs=gr.inputs.Image(type="filepath"),
outputs="text",
title="Image-to-Text Model",
description="Upload an image, and the model will describe it.",
)
# Launch the app
iface.launch()
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