app.py

import gradio as gr
import torch
from transformers import AutoModel, AutoTokenizer

# Load the model
model = AutoModel.from_pretrained("openbmb/MiniCPM-V-2", trust_remote_code=True)

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM-V-2", trust_remote_code=True)

model.eval()


# Image and text inputs for the interface
image = gr.Image(type="pil", label="Image")
question = gr.Textbox(label="Question")

# Output for the interface
answer = gr.Textbox(label="Predicted answer", show_label=True, show_copy_button=True)

title = "Sudoku Solver by FG"
description = "Sudoku Solver using MiniCPM-V-2 model by FG. Upload an image of a sudoku puzzle and ask a question to solve it."

# Define the function for solving Sudoku
def solve_sudoku(image, question):
    msgs = [{"role": "user", "content": question}]
    res = model.chat(
        image=image,
        msgs=msgs,
        context=None,
        tokenizer=tokenizer,
        sampling=True,
        temperature=0.7,
        stream=True,
        system_prompt="You are an AI assistant specialized in visual content analysis. Given an image and a related question, analyze the image thoroughly and provide a precise and informative answer based on the visible content. Ensure your response is clear, accurate, and directly addresses the question.",
    )
    return "".join(res)

# Create the Gradio interface
demo = gr.Interface(
    fn=solve_sudoku,
    inputs=[image, question],
    outputs=answer,
    title=title,
    description=description,
    theme="compact",
)

# Launch the interface
demo.launch(share=True)