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import torch
from transformers import AutoTokenizer, AutoModelForVision2Seq
from PIL import Image
import gradio as gr
# Load the model and tokenizer
model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForVision2Seq.from_pretrained(model_name)
# Function to classify the image and return the description
def classify_image(image):
# Ensure the image is in RGB mode
image = image.convert("RGB")
# Tokenize the image
inputs = tokenizer(image, return_tensors="pt", padding="max_length", truncation=True)
# Generate model output
with torch.no_grad():
outputs = model.generate(**inputs)
# Decode and return the result (description)
description = tokenizer.decode(outputs[0], skip_special_tokens=True)
return description
# Gradio Interface
interface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil", label="Upload an Image"), # Upload image dynamically
outputs=gr.Textbox(label="Description"),
title="Image Classification with Llama-3.2-11B-Vision-Instruct",
description="Upload an image and the model will describe what's in it."
)
# Launch the interface
interface.launch()
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