ajeetkumar01 commited on
Commit
f9a03ff
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1 Parent(s): 8d7fa74

Update app.py

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Files changed (1) hide show
  1. app.py +10 -21
app.py CHANGED
@@ -2,12 +2,12 @@ import torch
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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  import gradio as gr
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-
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  # Load pre-trained GPT-2 model and tokenizer
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  model_name = "gpt2-large"
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name)
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  def generate_text(input_text, max_length=16, num_beams=5, do_sample=False, no_repeat_ngram_size=2):
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  """
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  Generate text based on the given input text.
@@ -29,6 +29,7 @@ def generate_text(input_text, max_length=16, num_beams=5, do_sample=False, no_re
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  generated_text = tokenizer.decode(output[0])
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  return generated_text
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  # def generate_text_with_nucleus_search(input_text, max_length=16, do_sample=True, top_p=0.9):
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  # """
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  # Generate text with nucleus sampling based on the given input text.
@@ -48,27 +49,15 @@ def generate_text(input_text, max_length=16, num_beams=5, do_sample=False, no_re
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  # generated_text = tokenizer.decode(output[0])
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  # return generated_text
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- # Create Gradio input interface
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- input_text_interface = gr.Textbox(lines=5, label="Input Text", placeholder="Enter text for generation...")
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-
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- # Create Gradio output interface for regular text generation
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- output_text_interface1 = gr.Textbox(label="Generated Text (Regular)", placeholder="Generated text will appear here...")
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- # Interface for regular text generation
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- interface1 = gr.Interface(generate_text, input_text_interface, output_text_interface1,
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- title="Text Generation with GPT-2",
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- description="Generate text using the GPT-2 model with regular generation method.",
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- allow_flagging="never")
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- # Create Gradio output interface for text generation with nucleus sampling
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- # output_text_interface2 = gr.Textbox(label="Generated Text (Nucleus Sampling)", placeholder="Generated text will appear here...")
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- # # Interface for text generation with nucleus sampling
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- # interface2 = gr.Interface(generate_text_with_nucleus_search, input_text_interface, output_text_interface2,
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- # title="Text Generation with Nucleus Sampling",
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- # description="Generate text using nucleus sampling with the GPT-2 model.",
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- # allow_flagging="never")
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- # Launch both interfaces
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- interface1.launch(share=True)
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- # interface2.launch(share=True)
 
 
 
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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  import gradio as gr
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  # Load pre-trained GPT-2 model and tokenizer
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  model_name = "gpt2-large"
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name)
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+
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  def generate_text(input_text, max_length=16, num_beams=5, do_sample=False, no_repeat_ngram_size=2):
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  """
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  Generate text based on the given input text.
 
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  generated_text = tokenizer.decode(output[0])
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  return generated_text
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+
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  # def generate_text_with_nucleus_search(input_text, max_length=16, do_sample=True, top_p=0.9):
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  # """
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  # Generate text with nucleus sampling based on the given input text.
 
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  # generated_text = tokenizer.decode(output[0])
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  # return generated_text
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+ # Create Gradio interface
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+ input_text = gr.Textbox(lines=10, label="Input Text", placeholder="Enter text for text generation...")
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+ output_text = gr.Textbox(label="Generated Text")
 
 
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+ gr.Interface(generate_text, input_text, output_text,
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+ title="Text Generation with GPT-2",
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+ description="Generate text using the GPT-2 model.",
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+ theme="default",
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+ allow_flagging="never").launch(share=True)