app.py

import gradio as gr
import time
import onnxruntime_genai as og
from huggingface_hub import snapshot_download
import os

# Load model and processor
local_dir = snapshot_download(
    repo_id="microsoft/Phi-3.5-vision-instruct-onnx",
    allow_patterns="cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/*"
)
model_folder = os.path.join(
    local_dir, "cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4"
)
model = og.Model(model_folder)
processor = model.create_multimodal_processor()
tokenizer_stream = processor.create_stream()

user_prompt = '<|user|>\n'
assistant_prompt = '<|assistant|>\n'
prompt_suffix = "<|end|>\n"

# Inference function
def ask_phi(image, question, max_length):
    start_time = time.time()
    
    prompt = f"{user_prompt}<|image_1|>\n{question}{prompt_suffix}{assistant_prompt}"
    images = og.Images.open(image)
    inputs = processor(prompt=prompt, images=images, return_tensors="pt")
    
    
    params = og.GeneratorParams(model)
    params.set_search_options(max_length=max_length)    
    params.set_inputs(inputs)
    generator = og.Generator(model, params)
    

    response = ""
    while not generator.is_done():
        generator.generate_next_token()

        new_token = generator.get_next_tokens()[0]
        output = tokenizer_stream.decode(new_token)
        print(output, end="", flush=True)
        response += output

    print(f"\nInference took {time.time() - start_time} seconds")
    
    del generator
    return response

# Gradio Interface
demo = gr.Interface(
      fn=ask_phi,
      inputs=[
          gr.Image(type="filepath", label="Upload Image"),
          gr.Textbox(label="Your Prompt"),
          gr.Slider(minimum=16, maximum=16384, step=16, value=5000, label="Context Length")
      ],
      outputs=gr.Textbox(label="Phi-3.5 Response"),
      title="Phi-3.5 Vision Instruct (ONNX)",
      description="Ask a question about an image using Phi-3.5 ONNX on CPU"
)

demo.launch(debug=True)