| import gradio as gr |
| from transformers import pipeline |
|
|
| |
| generator = pipeline( |
| "text-generation", |
| model="EleutherAI/gpt-neo-1.3B" |
| ) |
|
|
| |
| max_lengths = { |
| "social media post": 280, |
| "email newsletter": 800, |
| "product description": 600, |
| "ad copy": 400 |
| } |
|
|
| def generate_marketing_text(prompt, content_type, _, temperature=0.7): |
| """ |
| Generate marketing text using GPT-Neo based on topic and content type. |
| """ |
| |
| enhanced_prompt = f"Write a {content_type} for the following product: {prompt}\nMake it persuasive, professional, and engaging." |
|
|
| |
| max_length = max_lengths.get(content_type, 400) |
|
|
| |
| result = generator( |
| enhanced_prompt, |
| max_length=max_length, |
| temperature=temperature, |
| do_sample=True, |
| pad_token_id=50256 |
| ) |
|
|
| return result[0]['generated_text'] |
|
|
| |
| demo = gr.Interface( |
| fn=generate_marketing_text, |
| inputs=[ |
| gr.Textbox(lines=3, placeholder="Enter your product or topic here...", label="Topic"), |
| gr.Radio( |
| ["social media post", "email newsletter", "product description", "ad copy"], |
| label="Content Type", |
| value="social media post" |
| ), |
| gr.Slider(minimum=50, maximum=800, value=280, step=10, label="(Auto-set) Max Length", interactive=False), |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Creativity (Temperature)") |
| ], |
| outputs=gr.Textbox(lines=10, label="Generated Marketing Content"), |
| title="AdGenAI - Marketing Content Generator", |
| description="Free-tier friendly version using GPT-Neo 1.3B. Enter a topic and select the type of content you want to generate." |
| ) |
|
|
| |
| demo.launch() |
|
|
