Update app.py

app.py

@@ -1,19 +1,33 @@
 import gradio as gr
+from transformers import BartTokenizer, BartForConditionalGeneration
 import whisper
 
-def convert_to_text(audio_path : str) -> str:  
-    model = whisper.load_model("base")
-    result = model.transcribe(audio_path)
-    return result["text"]
+# Initialize the BART model and tokenizer
+MODEL_NAME = "facebook/bart-large-cnn"
+model = BartForConditionalGeneration.from_pretrained(MODEL_NAME)
+tokenizer = BartTokenizer.from_pretrained(MODEL_NAME)
 
-audio_input = gr.components.Audio(type="filepath")
+def convert_and_summarize(audio_path: str) -> str:
+    # Convert audio to text
+    whisper_model = whisper.load_model("base")
+    result = whisper_model.transcribe(audio_path)
+    transcribed_text = result["text"]
+    
+    # Summarize the transcribed text
+    inputs = tokenizer([transcribed_text], max_length=1024, truncation=True, return_tensors='pt')
+    summary_ids = model.generate(inputs['input_ids'])
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    
+    return summary
+
+audio_input = gr.inputs.Audio(type="filepath")
 
 # Interface for Gradio
 iface = gr.Interface(
-    fn=convert_to_text,
+    fn=convert_and_summarize,
     inputs=audio_input,
     outputs="text",
-    title="Free audio transcription",
+    title="Audio-to-Summarized-Text",
     description="Upload an audio here and get a bullet-point summary of its content.",
     theme="Monochrome",
     live=True,