gpt2 pre-trained model with streamlit data app framework

app.py

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+import streamlit as st
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Load the pre-trained GPT-2 model and tokenizer
+model_name = "gpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+
+# Define a text prompt
+prompt = st.text_area('Enter the prompt!')
+
+# Encode the prompt text and convert to tensor
+if prompt:
+    input_ids = tokenizer.encode(prompt, return_tensors='pt')
+    
+    # Generate text using the GPT-2 model
+    output = model.generate(
+        input_ids,
+        max_length=100,  # Maximum length of the generated text
+        num_return_sequences=1,  # Number of sequences to generate
+        no_repeat_ngram_size=2,  # Avoid repeating n-grams
+        top_k=50,  # Top-K sampling
+        top_p=0.95,  # Top-p (nucleus) sampling
+        temperature=0.7  # Sampling temperature
+    )
+    
+    # Decode the generated text to string
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    # Print the generated text
+    st.write(generated_text)