Create app.py

app.py

@@ -0,0 +1,48 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the model and tokenizer
+model_name = "gpt2-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Streamlit app
+st.title("Blog Post Generator (GPT-2 Large)")
+
+# Input area for the topic
+topic = st.text_area("Enter the topic for your blog post:")
+
+# Generate button
+if st.button("Generate Blog Post"):
+    if topic:
+        # Prepare the prompt
+        prompt = f"Write a blog post about {topic}:\n\n"
+        
+        # Tokenize the input
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        
+        # Generate text
+        with torch.no_grad():
+            output = model.generate(
+                input_ids,
+                max_length=500,
+                num_return_sequences=1,
+                no_repeat_ngram_size=2,
+                top_k=50,
+                top_p=0.95,
+                temperature=0.7
+            )
+        
+        # Decode the generated text
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        
+        # Display the generated blog post
+        st.subheader("Generated Blog Post:")
+        st.write(generated_text)
+    else:
+        st.warning("Please enter a topic.")
+
+# Add some information about the app
+st.sidebar.header("About")
+st.sidebar.info("This app uses the GPT-2 Large model to generate blog posts based on your input topic.")