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import spaces
import torch
import gradio as gr
from threading import Thread
from transformers import AutoTokenizer, AutoModelForCausalLM
# Install the necessary package for the model
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"},
shell=True)
# Initialize the tokenizer and model
model_id = "vikhyatk/moondream2"
revision = "2024-04-02"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
moondream = AutoModelForCausalLM.from_pretrained(
model_id, revision=revision, trust_remote_code=True,
torch_dtype=torch.bfloat16, device_map={"": "cuda"},
attn_implementation="flash_attention_2"
)
moondream.eval()
@spaces.GPU(duration=10)
def chatbot_response(img, text_input):
# Here we assume an encoded image processing if needed
image_embeds = moondream.encode_image(img)
inputs = tokenizer.encode(text_input, return_tensors="pt")
outputs = moondream.generate(inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Setting up Gradio Interface
with gr.Blocks(theme="Monochrome") as demo:
gr.Markdown("# AskMoondream Chatbot")
with gr.Row():
img = gr.Image(type="pil", label="Upload an Image")
text_input = gr.Textbox(label="Ask a question or describe an image", placeholder="Type here...")
with gr.Row():
submit = gr.Button("Submit")
response = gr.TextArea(label="Response", placeholder="Moondream's response will appear here...")
# Define what happens when the user interacts with the interface
submit.click(chatbot_response, inputs=[img, text_input], outputs=response)
text_input.submit(chatbot_response, inputs=[img, text_input], outputs=response)
# Launch the demo
demo.queue().launch()
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