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Running
on
Zero
Running
on
Zero
# Import libraries | |
import gradio as gr | |
import torch | |
from PIL import Image | |
from transformers import AutoModel, AutoTokenizer | |
import spaces | |
device="cuda" | |
# Load the model and tokenizer | |
model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, torch_dtype=torch.float16) | |
model = model.to(device='cuda') | |
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True) | |
model.eval() | |
# Define a function to generate a response | |
def generate_response(image, question): | |
msgs = [{'role': 'user', 'content': question}] | |
res = model.chat( | |
image=image, | |
msgs=msgs, | |
tokenizer=tokenizer, | |
sampling=True, | |
temperature=0.7, | |
stream=True | |
) | |
generated_text = "" | |
for new_text in res: | |
generated_text += new_text | |
return generated_text | |
# Create a Gradio interface | |
iface = gr.Interface( | |
fn=generate_response, | |
inputs=[gr.Image(type="pil"), "text"], | |
outputs="text", | |
title="Visual Question Answering - Financial charts analysis", | |
description="Input an image and a question related to the image to receive a response.", | |
theme='abidlabs/dracula_revamped' | |
) | |
# Launch the app | |
iface.launch(debug=True) | |