Upload app.py

app.py

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+import whisper
+from pytube import YouTube
+import requests, io
+from urllib.request import urlopen
+from PIL import Image
+import time
+import streamlit as st
+from streamlit_lottie import st_lottie
+import numpy as np
+import os
+
+st.set_page_config(page_title="Youtube Transcriber", page_icon="🗣", layout="wide")
+
+
+# Define a function that we can use to load lottie files from a link.
+@st.cache(allow_output_mutation=True)
+def load_lottieurl(url: str):
+    r = requests.get(url)
+    if r.status_code != 200:
+        return None
+    return r.json()
+
+col1, col2 = st.columns([1, 3])
+with col1:
+    lottie = load_lottieurl("https://assets9.lottiefiles.com/private_files/lf30_bntlaz7t.json")
+    st_lottie(lottie, speed=1, height=200, width=200)
+
+with col2:
+    st.write("""
+    ## Youtube Transcriber 
+    ##### This is an app that transcribes YouTube videos into text.""")
+
+
+#def load_model(size):
+    #default_size = size
+    #if size == default_size:
+        #return None
+    #else:
+        #loaded_model = whisper.load_model(size)
+        #return loaded_model 
+    
+
+@st.cache(allow_output_mutation=True)
+def populate_metadata(link):
+    yt = YouTube(link)
+    author = yt.author
+    title = yt.title
+    description = yt.description
+    thumbnail = yt.thumbnail_url
+    length = yt.length
+    views = yt.views
+    return author, title, description, thumbnail, length, views
+
+# Uncomment if you want to fetch the thumbnails as well.
+#def fetch_thumbnail(thumbnail):
+    #tnail = urlopen(thumbnail)
+    #raw_data = tnail.read()
+    #image = Image.open(io.BytesIO(raw_data))
+    #st.image(image, use_column_width=True)
+
+
+def convert(seconds):
+    return time.strftime("%H:%M:%S", time.gmtime(seconds))
+
+
+loaded_model = whisper.load_model("base")
+current_size = "None"
+size = st.selectbox("Model Size", ["tiny", "base", "small", "medium", "large"], index=1)
+
+
+def change_model(current_size, size):
+    if current_size != size:
+        loaded_model = whisper.load_model(size)
+        st.write(f"Model is {'multilingual' if loaded_model.is_multilingual else 'English-only'} "
+        f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
+        return loaded_model
+    else:
+        return None
+
+
+@st.cache(allow_output_mutation=True)
+def inference(link):
+    yt = YouTube(link)
+    path = yt.streams.filter(only_audio=True)[0].download(filename="audio.mp4")
+    results = loaded_model.transcribe(path)
+    return results["text"]
+
+
+def main():
+    change_model(current_size, size)
+    link = st.text_input("YouTube Link")
+    if st.button("Transcribe"):
+        author, title, description, thumbnail, length, views = populate_metadata(link)
+        results = inference(link)
+            
+        col3, col4 = st.columns(2)
+        with col3:
+            #fetch_thumbnail(thumbnail)
+            st.video(link)
+            st.markdown(f"**Channel**: {author}")
+            st.markdown(f"**Title**: {title}")
+            st.markdown(f"**Length**: {convert(length)}")
+            st.markdown(f"**Views**: {views:,}")
+
+        with col4:
+            with st.expander("Video Description"):
+                st.write(description)
+            #st.markdown(f"**Video Description**: {description}")
+            with st.expander("Video Transcript"):
+                st.write(results)
+            # Write the results to a .txt file and download it.
+            with open("transcript.txt", "w+") as f:
+                f.writelines(results)
+                f.close()
+            with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
+                data = f.read()
+                if st.download_button(label="Download Transcript",
+                                data=data,
+                                file_name="transcript.txt"):
+                    st.success("Downloaded Successfully!")
+
+if __name__ == "__main__":
+    main()