Update app.py

app.py

@@ -1,9 +1,4 @@
-import requests
 import gradio as gr
-import os
-import time
-from pytubefix import YouTube
-from moviepy.editor import VideoFileClip
 from transformers import pipeline
 
 # Whisper model
@@ -12,45 +7,8 @@ asr = pipeline("automatic-speech-recognition", model="distil-whisper/distil-smal
 # Summarization model
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Function to fetch PO Token from the Node.js server
-def get_po_token():
+def process_audio(audio_path):
     try:
-        response = requests.get("http://localhost:3001/get-token")  # Call Node.js API
-        response.raise_for_status()
-        return response.json().get("poToken")  # Extract token
-    except Exception as e:
-        print(f"Error fetching PO token: {e}")
-        return None  # Return None if the request fails
-
-def process_youtube_link(youtube_url):
-    try:
-        print(f"Received YouTube URL: {youtube_url}")  # Debugging
-
-        po_token = get_po_token()
-        if not po_token:
-            return "Error: Unable to fetch PO token.", ""
-
-        # Use the token with pytubefix
-        yt = YouTube(youtube_url, po_token=po_token)
-        title = yt.title
-        print(f"Downloading: {title}")
-
-        ys = yt.streams.get_highest_resolution()
-        video_path = f"{title}.mp4"
-        ys.download(filename=video_path)
-
-        # Log download success
-        if os.path.exists(video_path):
-            print(f"Download successful: {video_path}")
-            print("Files in current directory:", os.listdir("."))
-        else:
-            print("Download failed!")
-
-        # Extract Audio
-        audio_path = f"{title}.wav"
-        video = VideoFileClip(video_path)
-        video.audio.write_audiofile(audio_path, codec="pcm_s16le")
-
         # Transcribe Audio
         transcription = asr(audio_path, return_timestamps=True)
         transcribed_text = transcription["text"]
@@ -59,18 +17,17 @@ def process_youtube_link(youtube_url):
         summary = summarizer(transcribed_text, max_length=150, min_length=50, do_sample=False)[0]["summary_text"]
 
         return transcribed_text, summary
-
     except Exception as e:
         print(f"Error: {str(e)}")  # Log errors
         return f"Error: {str(e)}", ""
 
 # Gradio Interface
 iface = gr.Interface(
-    fn=process_youtube_link,
-    inputs=gr.Textbox(label="Enter YouTube URL"),
+    fn=process_audio,
+    inputs=gr.Audio(source="upload", type="filepath"),
     outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Summary")],
-    title="YouTube Video Summarizer",
-    description="Enter a YouTube link, and this app will summarize the content of the video.",
+    title="Audio Summarizer",
+    description="Upload an audio file, and this app will transcribe and summarize its content.",
 )
 
-iface.launch()
+iface.launch()