Create app.py

app.py

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+import streamlit as st
+from transformers import GPT2Tokenizer, GPT2Model
+
+# Load pre-trained GPT-2 model and tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = GPT2Model.from_pretrained('gpt2')
+
+# Create a text area for the user to input the topic
+topic = st.text_area("Enter a topic to generate a blog post", height=275)
+
+# Create sliders for the user to specify the max length, temperature, top-k, and top-p
+max_length = st.sidebar.slider("Max Length", min_value=10, max_value=30)
+temperature = st.sidebar.slider("Temperature", value=1.0, min_value=0.0, max_value=1.0, step=0.05)
+top_k = st.sidebar.slider("Top-k", min_value=0, max_value=5, value=0)
+top_p = st.sidebar.slider("Top-p", min_value=0.0, max_value=1.0, step=0.05, value=0.9)
+num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=5, value=1, step=1)
+
+# Define a function to generate the blog post
+def generate_blogpost(topic):
+    # Encode the topic using the tokenizer
+    encoded_input = tokenizer(topic, return_tensors='pt')
+    
+    # Generate text using the model
+    output = model(**encoded_input)
+    generated_text = tokenizer.decode(output.last_hidden_state[:, 0, :], skip_special_tokens=True)
+    
+    return generated_text
+
+# Create a button to generate the blog post
+if st.button("Generate Blog Post"):
+    generated_text = generate_blogpost(topic)
+    st.write(generated_text)