Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# Load the GPT-2 tokenizer and model
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("gpt2")
+
+# Set the maximum length of generated text
+max_length = 200
+
+# Define a function to generate text
+def generate_text(prompt):
+    # Encode the prompt
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+    # Generate text
+    output = model.generate(
+        input_ids=input_ids,
+        max_length=max_length,
+        num_beams=5,
+        no_repeat_ngram_size=2,
+        early_stopping=True
+    )
+
+    # Decode the generated text
+    text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    return text
+
+# Set up the Streamlit app
+st.title("GPT-2 Text Generator")
+
+# Add a text input widget for the user to enter a prompt
+prompt = st.text_input("Enter a prompt:")
+
+# When the user clicks the "Generate" button, generate text
+if st.button("Generate"):
+    with st.spinner("Generating text..."):
+        text = generate_text(prompt)
+        st.write(text)