Update app.py

app.py

@@ -1,3 +1,68 @@
+# import torch
+# from transformers import AutoTokenizer, AutoModelForCausalLM
+# import gradio as gr
+
+# # Load pre-trained GPT-2 model and tokenizer
+# model_name = "gpt2-large"
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+# model = AutoModelForCausalLM.from_pretrained(model_name)
+
+
+# def generate_text(input_text, max_length=32, 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']
+#     # 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']
+# #     # 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_text = gr.Textbox(lines=10, label="Input Text", placeholder="Enter text for text generation...")
+# output_text = gr.Textbox(label="Generated Text")
+
+
+
+# gr.Interface(generate_text, input_text, output_text,
+#              title="Text Generation with GPT-2",
+#              description="Generate text using the GPT-2 model.",
+#              theme="default", 
+#              allow_flagging="never").launch(share=True)
+
+
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
@@ -5,8 +70,7 @@ import gradio as gr
 # Load pre-trained GPT-2 model and tokenizer
 model_name = "gpt2-large"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
+model = AutoModelForCausalLM.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id)
 
 def generate_text(input_text, max_length=32, num_beams=5, do_sample=False, no_repeat_ngram_size=2):
     """
@@ -21,43 +85,22 @@ def generate_text(input_text, max_length=32, num_beams=5, do_sample=False, no_re
     - 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']
+    input_ids = tokenizer(input_text, return_tensors='pt', padding=True)['input_ids']
     # 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])
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
     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']
-#     # 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_text = gr.Textbox(lines=10, label="Input Text", placeholder="Enter text for text generation...")
 output_text = gr.Textbox(label="Generated Text")
 
-
-
 gr.Interface(generate_text, input_text, output_text,
              title="Text Generation with GPT-2",
              description="Generate text using the GPT-2 model.",
              theme="default", 
-             allow_flagging="never").launch(share=True)
+             allow_flagging="never").launch()
+