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# Import required libraries - packages
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
@spaces.GPU
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 the footer with links
footer = """
<div style="text-align: center; margin-top: 20px;">
<a href="https://www.linkedin.com/in/pejman-ebrahimi-4a60151a7/" target="_blank">LinkedIn</a> |
<a href="https://github.com/arad1367/Visual_QA_MiniCPM-Llama3-V-2_5_GradioApp" target="_blank">GitHub</a> |
<a href="https://arad1367.pythonanywhere.com/" target="_blank">Live demo of my PhD defense</a>
<br>
Made with 💖 by Pejman Ebrahimi
</div>
"""
# Create a Gradio interface using gr.Blocks
with gr.Blocks(theme='abidlabs/dracula_revamped') as demo:
gr.Markdown("Visual Question Answering - Complete chart and image analysis")
gr.Markdown("Input an image and a question related to the image to receive a response.")
image_input = gr.Image(type="pil", label="Image")
question_input = gr.Textbox(label="Question")
output_text = gr.Textbox(label="Response")
image_input.change(generate_response, inputs=[image_input, question_input], outputs=output_text)
gr.HTML(footer)
# Launch the app
demo.launch(debug=True)
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