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import torch
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image
import gradio as gr
# Load the MAGMA model and processor
model_id = "microsoft/Magma-8B"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, trust_remote_code=True)
model.to("cuda" if torch.cuda.is_available() else "cpu")
def magma_inference(image: Image.Image, prompt: str) -> str:
# Prepare conversation
convs = [
{"role": "system", "content": "You are an agent that can see, talk, and act."},
{"role": "user", "content": prompt}
]
# Generate prompt
text_prompt = processor.tokenizer.apply_chat_template(convs, tokenize=False, add_generation_prompt=True)
# Process inputs
inputs = processor(images=[image], texts=text_prompt, return_tensors="pt").to(model.device)
# Generate output
with torch.inference_mode():
generate_ids = model.generate(**inputs, max_new_tokens=50)
generate_ids = generate_ids[:, inputs["input_ids"].shape[-1]:]
response = processor.decode(generate_ids[0], skip_special_tokens=True).strip()
return response
# Gradio interface
interface = gr.Interface(
fn=magma_inference,
inputs=[
gr.Image(type="pil", label="Input Image"),
gr.Textbox(label="Prompt")
],
outputs=gr.Textbox(label="MAGMA Output"),
title="MAGMA Image + Text to Text API",
description="Upload an image and enter a prompt. Returns MAGMA's textual response."
)
app = gr.mount_gradio_app(app=interface, path="/")
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