Update app.py

app.py

@@ -1,62 +1,50 @@
 import gradio as gr
-
 import os
-
-
-
+from moviepy.editor import VideoFileClip
 from transformers import pipeline
 
-# Load ASR (Speech-to-Text) pipeline with timestamp handling
+# Load ASR (Speech-to-Text) pipeline
 asr = pipeline(task="automatic-speech-recognition", model="distil-whisper/distil-small.en")
 
-
-
-
 # Load Summarization model
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Function to transcribe and summarize audio
-def transcribe_and_summarize(audio_file):
-    if audio_file is None:
-        return "Error: No audio file provided.", ""
-
+def transcribe_and_summarize(video_file):
+    if video_file is None:
+        return "Error: No file provided.", ""
 
     try:
-        # Transcribe audio (handling long-form audio)
-        transcription_result = asr(audio_file, return_timestamps=True)
-        
-        # Extract transcribed text
+        # Extract audio from the video file
+        video = VideoFileClip(video_file)
+        audio_path = "temp_audio.wav"
+        video.audio.write_audiofile(audio_path, codec='pcm_s16le')
+
+        # Transcribe the extracted audio
+        transcription_result = asr(audio_path, return_timestamps=True)
         transcribed_text = " ".join([segment['text'] for segment in transcription_result['chunks']])
 
-        # Ensure the transcribed text isn't too short for summarization
+        # Summarize if long enough
         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
+    inputs=gr.Video(type="filepath", label="Upload Video (.mp4)"),
     outputs=[
-        gr.Textbox(label="Transcribed Text"),  
+        gr.Textbox(label="Transcribed Text"),
         gr.Textbox(label="Summarized Text")
     ]
 )
 
-# Get port safely (default to 7860 if not set)
+# Launch app
 port = int(os.environ.get('PORT1', 7860))
-
-# Launch Gradio app
-iface.launch(share=True, server_port=port)
+iface.launch(share=True, server_port=port)