chore: Update app.py with improved Gradio interface for Sudoku Solver

app.py

@@ -1,18 +1,31 @@
 import gradio as gr
 from transformers import pipeline
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+# Initialize the pipeline
+pipe = pipeline("visual-question-answering", model="openbmb/MiniCPM-Llama3-V-2_5", trust_remote_code=True)
 
-def predict(input_img):
-    predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
+# Define the Gradio components
+image = gr.Image(type="pil", label="Image")
+question = gr.Textbox(value="Using the standard 9x9 sudoku format, solve the sudoku puzzle in the image correctly.", label="Question")
+answer = gr.Textbox(label="Answer", show_label=True, show_copy_button=True)
 
-gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="Hot Dog? Or Not?",
+title = "Sudoku Solver by FG"
+description = "Sudoku Solver using MiniCPM-Llama3-V-2_5"
+
+# Define the function for solving Sudoku
+def solve_sudoku(image, question):
+    result = pipe(image, question)
+    return result[0]['answer']
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=solve_sudoku,
+    inputs=[image, question],
+    outputs=answer,
+    title=title,
+    description=description,
+    theme="compact",
 )
 
-if __name__ == "__main__":
-    gradio_app.launch()
+# Launch the interface
+demo.launch(share=True)

requirements.txt

@@ -1 +1,2 @@
-transformers
+transformers
+torch