added main file

app.py

@@ -0,0 +1,71 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Check if GPU is available, otherwise use CPU
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load pre-trained GPT-2 model and tokenizer
+model_name = "gpt2-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+
+
+def generate_text(input_text, max_length=16, num_beams=5, do_sample=False, no_repeat_ngram_size=2):
+    """
+    Generate text based on the given input text.
+
+    Parameters:
+    - input_text (str): The input text to start generation from.
+    - max_length (int): Maximum length of the generated text.
+    - num_beams (int): Number of beams for beam search.
+    - do_sample (bool): Whether to use sampling or not.
+    - no_repeat_ngram_size (int): Size of the n-gram to avoid repetition.
+
+    Returns:
+    - generated_text (str): The generated text.
+    """
+    # Encode the input text and move it to the appropriate device
+    input_ids = tokenizer(input_text, return_tensors='pt')['input_ids'].to(device)
+    # Generate text using the model
+    output = model.generate(input_ids, max_length=max_length, num_beams=num_beams,
+                            do_sample=do_sample, no_repeat_ngram_size=no_repeat_ngram_size)
+    # Decode the generated output
+    generated_text = tokenizer.decode(output[0])
+    return generated_text
+
+
+def generate_text_with_nucleus_search(input_text, max_length=16, do_sample=True, top_p=0.9):
+    """
+    Generate text with nucleus sampling based on the given input text.
+
+    Parameters:
+    - input_text (str): The input text to start generation from.
+    - max_length (int): Maximum length of the generated text.
+    - do_sample (bool): Whether to use sampling or not.
+    - top_p (float): Nucleus sampling parameter.
+
+    Returns:
+    - generated_text (str): The generated text.
+    """
+    # Encode the input text and move it to the appropriate device
+    input_ids = tokenizer(input_text, return_tensors='pt')['input_ids'].to(device)
+    # Generate text using nucleus sampling
+    output = model.generate(input_ids, max_length=max_length, do_sample=do_sample, top_p=top_p)
+    # Decode the generated output
+    generated_text = tokenizer.decode(output[0])
+    return generated_text
+
+
+# Create Gradio interface
+input_textbox = gr.Textbox(lines=7, label="Input Text", placeholder="Enter your text here...")
+output_textbox = gr.Textbox(label="Generated Text", placeholder="Generated text will appear here...")
+
+gr.Interface(
+    [generate_text, generate_text_with_nucleus_search],
+    inputs=input_textbox,
+    outputs=output_textbox,
+    title="Text Generation with GPT-2",
+    description="Enter some text and generate new text using GPT-2 model.",
+    allow_flagging=False
+).launch(share=True)