Update app.py

app.py

@@ -132,74 +132,58 @@ def main():
     np.random.seed(seed)
 
     # Rutas de archivos
-    sample_file = 'Preludes 2 Through Major keys 39.mid'
+    #sample_file = 'Preludes 2 Through Major keys 39.mid'
     out_file = 'output.mid'
-    #sample_file = 
-    st.file_uploader("Sube un archivo MIDI")
-
-    # if sample_file is not None:
-    # # To read file as bytes:
-    #     bytes_data = sample_file.getvalue()
-    #     st.write(bytes_data)
-
-    # # To convert to a string based IO:
-    #     stringio = StringIO(sample_file.getvalue().decode("utf-8"))
-    #     st.write(stringio)
-
-    # # To read file as string:
-    #     string_data = stringio.read()
-    #     st.write(string_data)
-
-    # # Can be used wherever a "file-like" object is accepted:
-    #     dataframe = pd.read_csv(uploaded_file)
-    #     st.write(dataframe)
-
-    # Cargar modelo y pesos
-    with custom_object_scope({'mse_with_positive_pressure': mse_with_positive_pressure}):
-        model = keras.models.load_model("mi_modelo_music.h5")
-
-    model.load_weights("mi_pesos_music.h5", skip_mismatch=False, by_name=False, options=None)
-
-    # Convertir MIDI generado por el modelo a archivo WAV
-    pm = pretty_midi.PrettyMIDI(sample_file)
-    instrument_name = pretty_midi.program_to_instrument_name(pm.instruments[0].program)
-    raw_notes = midi_to_notes(sample_file)
-    key_order = ['pitch', 'step', 'duration']
-    seq_length = 25
-    vocab_size = 128
-    temperature = 2.0
-    num_predictions = 120
-    sample_notes = np.stack([raw_notes[key] for key in key_order], axis=1)
-    input_notes = (sample_notes[:seq_length] / np.array([vocab_size, 1, 1]))
-    generated_notes = []
-    prev_start = 0
-    for _ in range(num_predictions):
-        pitch, step, duration = predict_next_note(input_notes, model, temperature)
-        start = prev_start + step
-        end = start + duration
-        input_note = (pitch, step, duration)
-        generated_notes.append((*input_note, start, end))
-        input_notes = np.delete(input_notes, 0, axis=0)
-        input_notes = np.append(input_notes, np.expand_dims(input_note, 0), axis=0)
-        prev_start = start
-
-    generated_notes = pd.DataFrame(
-        generated_notes, columns=(*key_order, 'start', 'end'))
-
-    notes_to_midi(
-        generated_notes, out_file=out_file, instrument_name=instrument_name)
-
-    # Interfaz de Streamlit
-    st.title("Generador de notas musicales")
-
-    archivo_midi = open(out_file, 'rb').read()
+    sample_file = st.file_uploader("Sube un archivo MIDI")
+
+    if sample_file is not None:
+  
+        # Cargar modelo y pesos
+        with custom_object_scope({'mse_with_positive_pressure': mse_with_positive_pressure}):
+            model = keras.models.load_model("mi_modelo_music.h5")
     
-    st.download_button(
-        label="Descargar MIDI",
-        data=archivo_midi,
-        file_name=out_file,  # Nombre del archivo que se descargará
-        mime='audio/midi'  
-    )
+        model.load_weights("mi_pesos_music.h5", skip_mismatch=False, by_name=False, options=None)
+    
+        # Convertir MIDI generado por el modelo a archivo WAV
+        pm = pretty_midi.PrettyMIDI(sample_file)
+        instrument_name = pretty_midi.program_to_instrument_name(pm.instruments[0].program)
+        raw_notes = midi_to_notes(sample_file)
+        key_order = ['pitch', 'step', 'duration']
+        seq_length = 25
+        vocab_size = 128
+        temperature = 2.0
+        num_predictions = 120
+        sample_notes = np.stack([raw_notes[key] for key in key_order], axis=1)
+        input_notes = (sample_notes[:seq_length] / np.array([vocab_size, 1, 1]))
+        generated_notes = []
+        prev_start = 0
+        for _ in range(num_predictions):
+            pitch, step, duration = predict_next_note(input_notes, model, temperature)
+            start = prev_start + step
+            end = start + duration
+            input_note = (pitch, step, duration)
+            generated_notes.append((*input_note, start, end))
+            input_notes = np.delete(input_notes, 0, axis=0)
+            input_notes = np.append(input_notes, np.expand_dims(input_note, 0), axis=0)
+            prev_start = start
+    
+        generated_notes = pd.DataFrame(
+            generated_notes, columns=(*key_order, 'start', 'end'))
+    
+        notes_to_midi(
+            generated_notes, out_file=out_file, instrument_name=instrument_name)
+    
+        # Interfaz de Streamlit
+        st.title("Generador de notas musicales")
+    
+        archivo_midi = open(out_file, 'rb').read()
+        
+        st.download_button(
+            label="Descargar MIDI",
+            data=archivo_midi,
+            file_name=out_file,  # Nombre del archivo que se descargará
+            mime='audio/midi'  
+        )
 
 if __name__ == "__main__":
     main()