Add application file

app.py

@@ -0,0 +1,91 @@
+import streamlit as st
+from models import BagOfModels, SoundToText, TextToSummary
+from settings import MODEL_PARSER
+args = MODEL_PARSER
+
+st.set_page_config(
+    page_title="TTS Applications | Incore Solutions",
+    layout="wide",
+    menu_items={
+        "About": """This is a simple GUI for OpenAI's Whisper.""",
+    },
+)
+
+def open_instructions():
+    with open("instructions.md", "r") as f:
+        st.write(f.read())
+
+# Render input type selection on the sidebar & the form
+input_type = st.sidebar.selectbox("Input Type", ["YouTube", "File"])
+
+with st.sidebar.form("input_form"):
+    if input_type == "YouTube":
+        youtube_url = st.text_input("Youtube URL")
+    elif input_type == "File":
+        input_file = st.file_uploader("File", type=["mp3", "wav"])       
+
+    whisper_model = st.selectbox("Whisper model", options = [whisper for whisper in BagOfModels.get_model_names() if "whisper" in whisper] , index=1) 
+
+    summary = st.checkbox("summarize")
+    if summary:
+        min_sum = st.number_input("Minimum words in the summary", min_value=1, step=1)
+        max_sum = min(min_sum,st.number_input("Maximum words in the summary", min_value=2, step=1))
+    st.form_submit_button(label="Save settings")
+with st.sidebar.form("save settings"):
+    transcribe = st.form_submit_button(label="Transcribe!")
+   
+   
+if transcribe:
+    if input_type == "YouTube":
+        if youtube_url and youtube_url.startswith("http"):
+            model = BagOfModels.load_model(whisper_model,**vars(args))
+            st.session_state.transcription = model.predict_stt(source=youtube_url,source_type=input_type,model_task="stt")
+        else:
+            st.error("Please enter a valid YouTube URL")
+            open_instructions()
+        
+    elif input_type == "File":
+        if input_file:
+            model = BagOfModels.load_model(whisper_model,**vars(args))
+            st.session_state.transcription = model.predict_stt(source=input_file,source_type=input_type,model_task="stt")
+        else:
+            st.error("Please upload a file")
+
+if "transcription" in st.session_state:
+    st.session_state.transcription.whisper()
+
+    # create two columns to separate page and youtube video
+    transcription_col, media_col = st.columns(2, gap="large")
+
+    transcription_col.markdown("#### Audio")
+    with open(st.session_state.transcription.audio_path, "rb") as f:
+        transcription_col.audio(f.read())
+    transcription_col.markdown("---")
+    transcription_col.markdown(f"#### Transcription (whisper model - `{whisper_model}`)")
+    transcription_col.markdown(f"##### Language: `{st.session_state.transcription.language}`")
+
+    # Trim raw transcribed output off tokens to simplify
+    raw_output = transcription_col.expander("Raw output")
+    raw_output.markdown(st.session_state.transcription.raw_output["text"])
+
+    if summary:
+        summarized_output = transcription_col.expander("summarized output")
+        # CURRENTLY ONLY SUPPORTS 1024 WORD TOKENS -> TODO: FIND METHOD TO INCREASE SUMMARY FOR LONGER VIDS -> 1024 * 4 = aprox 800 words within 1024 range
+        text_summary = TextToSummary(str(st.session_state.transcription.text[:1024*4]),min_sum,max_sum).get_summary()
+        summarized_output.markdown(text_summary[0]["summary_text"])    
+
+    # Show transcription in format with timers added to text
+    time_annotated_output = transcription_col.expander("time_annotated_output")
+    for segment in st.session_state.transcription.segments:
+        time_annotated_output.markdown(
+            f"""[{round(segment["start"], 1)} - {round(segment["end"], 1)}] - {segment["text"]}"""
+        )
+    
+    # Show input youtube video
+    if input_type == "YouTube":
+        media_col.markdown("---")
+        media_col.markdown("#### Original YouTube Video")
+        media_col.video(st.session_state.transcription.source)
+else:
+    pass
+