app.py

#imagetext-to-text
#huggingface spaces
import gradio as gr
import base64
from huggingface_hub import InferenceClient
client = InferenceClient('meta-llama/Llama-3.2-11B-Vision-Instruct')

def imageDescription(image, prompt):
  image_path="image.png"
  image.save(image_path)
  with open(image_path, "rb") as f:
    base64_image = base64.b64encode(f.read()).decode("utf-8")
  image_url = f"data:image/png;base64,{base64_image}"
  output = client.chat.completions.create(messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {"url": image_url},
                },
                {
                    "type": "text",
                    "text": prompt,
                },
            ],
        },
    ],
  )
  return output.choices[0].message.content

with gr.Blocks(theme=gr.themes.Citrus()) as demo:
    with gr.Row():
      with gr.Column():
        #an image input
        image=gr.Image(type="pil", label="upload an immage")
        prompt = gr.Textbox(label="What would you like to know about this picture?",scale=1)
        describe_btn = gr.Button("Describe the image",scale=1)
        output = gr.Textbox(label="Description",scale=1)
      with gr.Column():
        #sending two inputs to imageDescription function
        describe_btn.click(fn=imageDescription, inputs=[image, prompt], outputs=output)
demo.launch(debug=True)