Create app.py

app.py

@@ -0,0 +1,49 @@
+import whisper
+from moviepy.editor import VideoFileClip
+from transformers import BartForConditionalGeneration, BartTokenizer
+import gradio as gr
+import os
+
+# Load models
+whisper_model = whisper.load_model("base")
+tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
+summarizer_model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
+
+def transcribe_and_summarize(video_file):
+    try:
+        # Step 1: Process the video
+        clip = VideoFileClip(video_file)
+        audio_file = "audio.wav"
+        clip.audio.write_audiofile(audio_file)
+
+        # Step 2: Transcribe audio
+        result = whisper_model.transcribe(audio_file)
+        transcribed_text = result['text']
+
+        # Step 3: Summarize text
+        inputs = tokenizer(transcribed_text, return_tensors="pt", max_length=1024, truncation=True)
+        summary_ids = summarizer_model.generate(inputs["input_ids"], max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+        # Clean up the audio file
+        os.remove(audio_file)
+
+        return transcribed_text, summary
+
+    except Exception as e:
+        return str(e), "Error occurred during processing."
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=transcribe_and_summarize,
+    inputs=gr.Video(label="Upload Video"),
+    outputs=[
+        gr.Textbox(label="Transcribed Text", lines=10),
+        gr.Textbox(label="Summary", lines=5)
+    ],
+    title="Video Transcription and Summarization",
+    description="Upload a video file to transcribe its audio and generate a summary of the transcribed text."
+)
+
+# Launch the interface
+iface.launch()