Update app.py

app.py

@@ -7,11 +7,6 @@ import gradio as gr
 from transformers import pipeline
 
 text_summary = pipeline("summarization", model="sshleifer/distilbart-cnn-6-6")
-# model_path = ("../Models/models--sshleifer--distilbart-cnn-6-6/snapshots/d2fde4ca965ba893255479612e4b801aa6500029")
-
-# text_summary = pipeline("summarization", model=model_path,
-#                 torch_dtype=torch.bfloat16)
-
 
 def split_text_to_chunks(text, chunk_size=1024):
     return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
@@ -43,17 +38,10 @@ def get_transcript(video_url):
     except Exception as e:
         return f"Error fetching transcript: {e}"
 
-# youtube_url = input("Enter YouTube URL: ")
-# transcript = get_transcript(youtube_url)
-# output = summary(transcript)
-# print("\n--- Video Transcript ---\n")
-# print(output)
-
 gr.close_all()
-# demo = gr.Interface(fn=summary,inputs="text", outputs="text")
 demo = gr.Interface(fn=get_transcript,
-                    inputs=[gr.Textbox(label="Input text to summarize", lines=1)],
+                    inputs=[gr.Textbox(label="Input Youtube URL to summarize the video", lines=1)],
                     outputs=[gr.Textbox(label="Summarized text", lines=4)],
-                    title="Text Summarizer",
-                    description="This application will be used to summarise the text")
+                    title="Youtube Summarizer",
+                    description="This application will be used to summarise the youtube video")
 demo.launch()