feat: incluye spinner loading icon y output block para los resultados

app.py

@@ -27,33 +27,37 @@ def convert_mp3_to_wav(mp3_path):
     audio.export(wav_path, format="wav")
     return wav_path
 
-# Streamlit App
-st.title('Transcriptor de Audio')
-
-uploaded_file = st.file_uploader("Sube tu archivo de audio para transcribir", type=['wav', 'mp3'])
-
-if uploaded_file is not None:
-    file_details = {"FileName":uploaded_file.name, "FileType":uploaded_file.type, "FileSize":uploaded_file.size}
-    st.write(file_details)
-
-    # Save uploaded file to temp directory
-    file_path = os.path.join("temp", uploaded_file.name)
-    with open(file_path, "wb") as f:
-        f.write(uploaded_file.getbuffer())
-    
-    st.write("Archivo de audio cargado correctamente. Por favor, selecciona el método de transcripción.")
-    transcription_method = st.selectbox('Escoge el método de transcripción', ('OpenAI Whisper', 'Google Speech API'))
-
-    if transcription_method == 'OpenAI Whisper':
-        model_name = st.selectbox('Escoge el modelo de Whisper', ('base', 'small', 'medium', 'large', 'tiny'))
-    elif transcription_method == 'Google Speech API' and file_path.endswith('.mp3'):
-        # Convert mp3 to wav if Google Speech API is selected and file is in mp3 format
-        file_path = convert_mp3_to_wav(file_path)
-
-    if st.button('Transcribir'):
-        if transcription_method == 'OpenAI Whisper':
-            transcript = transcribe_whisper(model_name, file_path)
-        else:
-            transcript = transcribe_speech_recognition(file_path)
+def main():
+    st.title('Transcriptor de Audio')
+
+    uploaded_file = st.file_uploader("Sube tu archivo de audio para transcribir", type=['wav', 'mp3'])
+
+    if uploaded_file is not None:
+        file_details = {"FileName":uploaded_file.name, "FileType":uploaded_file.type, "FileSize":uploaded_file.size}
+        st.write(file_details)
 
-        st.write(transcript)
+        # Save uploaded file to temp directory
+        file_path = os.path.join("temp", uploaded_file.name)
+        with open(file_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        
+        st.write("Archivo de audio cargado correctamente. Por favor, selecciona el método de transcripción.")
+        transcription_method = st.selectbox('Escoge el método de transcripción', ('OpenAI Whisper', 'Google Speech API'))
+
+        if transcription_method == 'OpenAI Whisper':
+            model_name = st.selectbox('Escoge el modelo de Whisper', ('base', 'small', 'medium', 'large', 'tiny'))
+        elif transcription_method == 'Google Speech API' and file_path.endswith('.mp3'):
+            # Convert mp3 to wav if Google Speech API is selected and file is in mp3 format
+            file_path = convert_mp3_to_wav(file_path)
+
+        if st.button('Transcribir'):
+            with st.spinner('Transcribiendo...'):
+                if transcription_method == 'OpenAI Whisper':
+                    transcript = transcribe_whisper(model_name, file_path)
+                else:
+                    transcript = transcribe_speech_recognition(file_path)
+
+            st.text_area('Resultado de la Transcripción:', transcript, height=200)
+
+if __name__ == "__main__":
+    main()