Update app.py

app.py

@@ -1,60 +1,50 @@
+import gradio as gr
 import os
-import torch
-from pytubefix import YouTube
-from moviepy.editor import VideoFileClip
 from transformers import pipeline
-pip install moviepy
-# ---- STEP 1: Download YouTube Video ----
-url = "https://www.youtube.com/watch?v=VgxnyKnB3qc&ab"
-yt = YouTube(url)
-title = yt.title
-print(f"Downloading: {title}")
 
-video_stream = yt.streams.get_highest_resolution()
-video_path = f"/content/{title}.mp4"
-video_stream.download(filename=video_path)
-
-print(f"Video saved as: {video_path}")
-
-# ---- STEP 2: Extract Audio from Video ----
-output_audio = f"/content/{title}.wav"
-
-video = VideoFileClip(video_path)
-video.audio.write_audiofile(output_audio)
-
-print(f"Audio extracted: {output_audio}")
-
-# ---- STEP 3: Transcribe Audio ----
+# Load ASR (Speech-to-Text) pipeline with timestamp handling
 asr = pipeline(task="automatic-speech-recognition", model="distil-whisper/distil-small.en")
 
-def transcribe_audio(audio_file):
-    print("Transcribing audio...")
-    transcription_result = asr(audio_file)
-    transcribed_text = transcription_result["text"]
-    return transcribed_text
-
-transcribed_text = transcribe_audio(output_audio)
-print("Transcription Complete:\n", transcribed_text[:500])  # Preview first 500 characters
-
-# ---- STEP 4: Summarize Transcription ----
+# Load Summarization model
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-def summarize_text(text):
-    if len(text.split()) < 50:
-        return "Text too short to summarize."
-    
-    print("Summarizing text...")
-    summary_result = summarizer(text, max_length=100, min_length=30, do_sample=False)
-    return summary_result[0]['summary_text']
-
-summarized_text = summarize_text(transcribed_text)
-print("\nSummary:\n", summarized_text)
-
-# ---- OPTIONAL: Save Results to File ----
-with open(f"/content/{title}_transcription.txt", "w") as f:
-    f.write(transcribed_text)
-
-with open(f"/content/{title}_summary.txt", "w") as f:
-    f.write(summarized_text)
-
-print("Transcription & Summary saved!")
+# Function to transcribe and summarize audio
+def transcribe_and_summarize(audio_file):
+    if audio_file is None:
+        return "Error: No audio file provided.", ""
+
+    try:
+        # Transcribe audio (handling long-form audio)
+        transcription_result = asr(audio_file, return_timestamps=True)
+        
+        # Extract transcribed text
+        transcribed_text = " ".join([segment['text'] for segment in transcription_result['chunks']])
+
+        # Ensure the transcribed text isn't too short for summarization
+        if len(transcribed_text.split()) < 50:
+            summarized_text = "Text too short to summarize."
+        else:
+            # Summarize the transcribed text
+            summary_result = summarizer(transcribed_text, max_length=100, min_length=30, do_sample=False)
+            summarized_text = summary_result[0]['summary_text']
+
+        return transcribed_text, summarized_text
+
+    except Exception as e:
+        return f"Error: {str(e)}", ""
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=transcribe_and_summarize,
+    inputs=gr.Audio(type="filepath"),  # Accepts an audio file
+    outputs=[
+        gr.Textbox(label="Transcribed Text"),  
+        gr.Textbox(label="Summarized Text")
+    ]
+)
+
+# Get port safely (default to 7860 if not set)
+port = int(os.environ.get('PORT1', 7860))
+
+# Launch Gradio app
+iface.launch(share=True, server_port=port)