app.py

import gradio as gr
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the GPT-2 model and tokenizer
model_name = "openai-community/gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

def generate_transcript(input_text):
    # Split the input text into smaller parts
    sentences = input_text.split('. ')  # Simple split by sentences
    new_transcripts = []

    # Generate new transcript for each chunk
    for sentence in sentences:
        input_ids = tokenizer.encode(sentence, return_tensors='pt')

        # Generate new text
        with torch.no_grad():
            output = model.generate(input_ids, max_length=50, num_return_sequences=1)

        new_text = tokenizer.decode(output[0], skip_special_tokens=True)
        new_transcripts.append(new_text)

    # Combine all output to make the final transcript
    final_transcript = '. '.join(new_transcripts)
    
    return final_transcript

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_transcript,
    inputs=gr.Textbox(lines=10, placeholder="Enter video transcript here..."),
    outputs=gr.Textbox(),
    title="Transcript Generator",
    description="Enter a video transcript, and this app will generate a new, similar transcript using GPT-2."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()