app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import pipeline
+
+# Initialize the text generation pipeline
+generator = pipeline('text-generation', model='gpt2')
+
+def generate_blogpost(topic, max_length=500):
+    prompt = f"Write a blog post about {topic}:\n\n"
+    
+    # Generate the blog post
+    generated_text = generator(prompt, max_length=max_length, num_return_sequences=1)[0]['generated_text']
+    
+    # Remove the prompt from the generated text
+    blog_post = generated_text[len(prompt):].strip()
+    
+    return blog_post
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_blogpost,
+    inputs=[
+        gr.Textbox(lines=1, placeholder="Enter the blog post topic here..."),
+        gr.Slider(minimum=100, maximum=1000, step=50, label="Max Length", value=500)
+    ],
+    outputs="text",
+    title="Blog Post Generator",
+    description="Enter a topic, and this app will generate a blog post using GPT-2."
+)
+
+# Launch the app
+iface.launch()