Create app.py

app.py

@@ -0,0 +1,67 @@
+import gradio as gr
+import os
+
+os.environ["KERAS_BACKEND"] = "torch"
+import streamlit as st
+import numpy as np
+from keras import models, utils
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+def echo_sudoku(sudoku, model_name):
+    model = models.load_model(hf_hub_download(
+        repo_id="Ritvik19/SudokuNet",
+        filename=f"{model_name}.keras",
+    ))
+    puzzles = sudoku.copy().values.reshape(1, 9, 9)
+    for _ in range((puzzles == 0).sum((1, 2)).max()):
+        model_preds = model.predict(
+            utils.to_categorical(puzzles, num_classes=10), verbose=0
+        )
+        preds = np.zeros((puzzles.shape[0], 81, 9))
+        for i in range(9):
+            for j in range(9):
+                preds[:, i * 9 + j] = model_preds[f"position_{i+1}_{j+1}"]
+        probs = preds.max(2)
+        values = preds.argmax(2) + 1
+        zeros = (puzzles == 0).reshape((puzzles.shape[0], 81))
+        for grid, prob, value, zero in zip(puzzles, probs, values, zeros):
+            if any(zero):
+                where = np.where(zero)[0]
+                confidence_position = where[prob[zero].argmax()]
+                confidence_value = value[confidence_position]
+                grid.flat[confidence_position] = confidence_value
+    return puzzles[0]
+
+model_types = ['ffn', 'cnn', 'rnn', 'lstm', 'gru']
+model_sizes = ['64x2', '64x4', '128x2', '128x4']
+model_names = [f"{model_type}__{model_size}" for model_type in model_types for model_size in model_sizes]
+
+DEFAULT_PUZZLE = """
+0 0 4 3 0 0 2 0 9
+0 0 5 0 0 9 0 0 1
+0 7 0 0 6 0 0 4 3
+0 0 6 0 0 2 0 8 7
+1 9 0 0 0 7 4 0 0
+0 5 0 0 8 3 0 0 0
+6 0 0 0 0 0 1 0 5
+0 0 3 5 0 8 6 9 0
+0 4 2 9 1 0 3 0 0
+""".strip()
+DEFAULT_PUZZLE = np.array([int(digit) for digit in DEFAULT_PUZZLE.split()]).reshape(9, 9)
+
+
+interface = gr.Interface(
+    fn=echo_sudoku, 
+    inputs=[
+        gr.Dataframe(label="Input Sudoku Puzzle", datatype="number", row_count=9, col_count=9, value=DEFAULT_PUZZLE), 
+        gr.Dropdown(label="Select Model", choices=model_names, value="cnn__64x2")
+        ], 
+    outputs=gr.Dataframe(label="Input Sudoku Puzzle", datatype="number", row_count=9, col_count=9),
+    title="Sudoku Solver",
+    description='A demo app for <a href="https://ritvik19.github.io/sudoku-net" target="_blank">SudokuNet</a>'
+)
+
+# Run the app
+if __name__ == "__main__":
+    interface.launch(debug=True)