coded the streamlit app

app.py

@@ -1,4 +1,37 @@
+"""app.py"""
+
 import streamlit as st
+from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
+
+# Load pre-trained GPT-2 model and tokenizer
+model_name = "gpt2"
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+
+# Define function to generate blog post
+def generate_blogpost(topic):
+    input_text = f"Blog post about {topic}:"
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+
+    # Generate text
+    output = model.generate(input_ids, max_length=500, num_return_sequences=1, no_repeat_ngram_size=2)
+
+    # Decode and return text
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
+
+# Streamlit app
+def main():
+    st.title("Blog Post Generator")
+
+    # Sidebar input for topic
+    topic = st.sidebar.text_input("Enter topic for the blog post", "a crazy person driving a car")
+
+    # Generate button
+    if st.sidebar.button("Generate Blog Post"):
+        blogpost = generate_blogpost(topic)
+        st.subheader(f"Generated Blog Post on {topic}:")
+        st.write(blogpost)
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+if __name__ == "__main__":
+    main()