Save single audio file & script when completed in demo app (#42)

* Save audio & script when completed in demo app

* Add UI message

* [WIP]

* Save audio and script in streamlit session state

demo/app.py

@@ -1,8 +1,10 @@
 import re
 from pathlib import Path
 
+import numpy as np
 import streamlit as st
 
+from document_to_podcast.podcast_maker.script_to_audio import save_waveform_as_file
 from document_to_podcast.preprocessing import DATA_LOADERS, DATA_CLEANERS
 from document_to_podcast.inference.model_loaders import (
     load_llama_cpp_model,
@@ -48,6 +50,21 @@ def load_text_to_speech_model_and_tokenizer():
     return load_parler_tts_model_and_tokenizer("parler-tts/parler-tts-mini-v1", "cpu")
 
 
+script = "script"
+audio = "audio"
+gen_button = "generate podcast button"
+if script not in st.session_state:
+    st.session_state[script] = ""
+if audio not in st.session_state:
+    st.session_state.audio = []
+if gen_button not in st.session_state:
+    st.session_state[gen_button] = False
+
+
+def gen_button_clicked():
+    st.session_state[gen_button] = True
+
+
 st.title("Document To Podcast")
 
 st.header("Uploading Data")
@@ -123,15 +140,17 @@ def load_text_to_speech_model_and_tokenizer():
 
     system_prompt = st.text_area("Podcast generation prompt", value=PODCAST_PROMPT)
 
-    if st.button("Generate Podcast"):
+    if st.button("Generate Podcast", on_click=gen_button_clicked):
         with st.spinner("Generating Podcast..."):
             text = ""
             for chunk in text_to_text_stream(
                 clean_text, text_model, system_prompt=system_prompt.strip()
             ):
                 text += chunk
                 if text.endswith("\n") and "Speaker" in text:
-                    st.write(text)
+                    st.session_state.script += text
+                    st.write(st.session_state.script)
+
                     speaker_id = re.search(r"Speaker (\d+)", text).group(1)
                     with st.spinner("Generating Audio..."):
                         speech = text_to_speech(
@@ -141,4 +160,22 @@ def load_text_to_speech_model_and_tokenizer():
                             SPEAKER_DESCRIPTIONS[speaker_id],
                         )
                     st.audio(speech, sample_rate=speech_model.config.sampling_rate)
+                    st.session_state.audio.append(speech)
                     text = ""
+
+    if st.session_state[gen_button]:
+        if st.button("Save Podcast to audio file"):
+            st.session_state.audio = np.concatenate(st.session_state.audio)
+            save_waveform_as_file(
+                waveform=st.session_state.audio,
+                sampling_rate=speech_model.config.sampling_rate,
+                filename="podcast.wav",
+            )
+            st.markdown("Podcast saved to disk!")
+
+        if st.button("Save Podcast script to text file"):
+            with open("script.txt", "w") as f:
+                st.session_state.script += "}"
+                f.write(st.session_state.script)
+
+            st.markdown("Script saved to disk!")