Update app.py

app.py

@@ -36,12 +36,38 @@ if hf_token:
 else:
     raise ValueError("HF_TOKEN environment variable not set.")
     
+# from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+# import gradio as gr
+
+# # Load the model and tokenizer
+# tokenizer = AutoTokenizer.from_pretrained("machinelearningzuu/youtube-content-summarization")
+# model = AutoModelForSeq2SeqLM.from_pretrained("machinelearningzuu/youtube-content-summarization")
+
+# # Define a function for summarization
+# def summarize_youtube_content(input_text):
+#     # Use the pipeline for summarization
+#     summarizer = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
+#     summary = summarizer(input_text, max_length=150, min_length=30, do_sample=False)
+#     return summary[0]['generated_text']
+
+# # Create a Gradio interface
+# interface = gr.Interface(
+#     fn=summarize_youtube_content,
+#     inputs=gr.Textbox(lines=10, placeholder="Paste YouTube transcript here..."),
+#     outputs=gr.Textbox(lines=5, label="Summarized Content"),
+#     title="YouTube Content Summarizer",
+#     description="Paste the transcript of a YouTube video to generate a concise summary.",
+# )
+
+# # Launch the Gradio app
+# if __name__ == "__main__":
+#     interface.launch()
 from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 import gradio as gr
 
 # Load the model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("machinelearningzuu/youtube-content-summarization")
-model = AutoModelForSeq2SeqLM.from_pretrained("machinelearningzuu/youtube-content-summarization")
+tokenizer = AutoTokenizer.from_pretrained("machinelearningzuu/youtube-content-summarization-bart")
+model = AutoModelForSeq2SeqLM.from_pretrained("machinelearningzuu/youtube-content-summarization-bart")
 
 # Define a function for summarization
 def summarize_youtube_content(input_text):