Update app.py
Browse files
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.
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@@ -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.
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@@ -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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# 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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#
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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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-
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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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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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# 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)
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