Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from transformers import AutoProcessor, VisionEncoderDecoderModel
+
+# Load model
+processor = AutoProcessor.from_pretrained("truelitmus/omr-crnn")
+model = VisionEncoderDecoderModel.from_pretrained("truelitmus/omr-crnn")
+
+# Study course generator
+def generate_study_course(note_string):
+    note_list = note_string.split()
+    unique_notes = sorted(set(note_list))
+
+    course = {
+        "Scales": {
+            "All Notes": unique_notes,
+            "Suggested Practice": [f"{note} Major Scale" for note in unique_notes]
+        },
+        "Practice Exercises": {
+            "Sight Reading (4-note chunks)": [note_list[i:i+4] for i in range(0, len(note_list)-3, 4)],
+            "Finger Exercises": [f"{note} Arpeggio" for note in unique_notes]
+        }
+    }
+    return course
+
+# Main function
+def process_sheet(image: Image.Image):
+    inputs = processor(images=image.convert("RGB"), return_tensors="pt")
+    output = model.generate(**inputs)
+    notes = processor.batch_decode(output, skip_special_tokens=True)[0]
+    return generate_study_course(notes)
+
+# Gradio interface
+demo = gr.Interface(
+    fn=process_sheet,
+    inputs=gr.Image(type="pil", label="Upload Sheet Music Image"),
+    outputs=gr.JSON(label="Generated Study Course"),
+    title="🎼 Sheet Music Study AI",
+    description="Upload a sheet music image to detect notes and receive a structured study course with scales and practice suggestions.",
+)
+
+demo.launch()