created blogpost gen

app.py

@@ -1,9 +1,25 @@
+
 import streamlit as st
-from transformers import pipeline
+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)
 
-pipe = pipeline('sentiment-analysis')
-text = st.text_area('enter some text: ')
+    # Decode and return text
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
 
-if text:
-    out = pipe(text)
-    st.json(out)
+# Example usage
+topic = "natural language processing"
+blogpost = generate_blogpost(topic)
+print(blogpost)