Update app.py

app.py

@@ -1,9 +1,30 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-pipe = pipeline('sentiment-analysis')
-text = st.text_area('Enter some text!')
+# Load pre-trained model and tokenizer
+model_name = "gpt2"
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 
-if text:
-    out = pipe(text)
-    st.json(out)
+def generate_blog_post(topic, max_length=500):
+    # Encode the input text
+    input_ids = tokenizer.encode(topic, return_tensors='pt')
+    
+    # Generate text
+    outputs = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
+    
+    # Decode the generated text
+    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return text
+
+# Streamlit app
+st.title("Blog Post Generator")
+st.write("Enter a topic, and the model will generate a blog post for you.")
+
+topic = st.text_input("Topic", value="Artificial Intelligence")
+max_length = st.slider("Max Length", min_value=50, max_value=1000, value=500)
+
+if st.button("Generate Blog Post"):
+    with st.spinner("Generating..."):
+        blog_post = generate_blog_post(topic, max_length)
+        st.write(blog_post)