import gradio as gr
from transformers import pipeline

# Load GPT-Neo model, optimized for CPU usage
generator = pipeline(
    "text-generation",
    model="EleutherAI/gpt-neo-1.3B"  # Runs fine on CPU Basic tier
)

# Recommended length per content type
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.
    """
    # Smart prompt engineering for better output
    enhanced_prompt = f"Write a {content_type} for the following product: {prompt}\nMake it persuasive, professional, and engaging."

    # Choose max_length based on content type
    max_length = max_lengths.get(content_type, 400)

    # Generate text using the model
    result = generator(
        enhanced_prompt,
        max_length=max_length,
        temperature=temperature,
        do_sample=True,
        pad_token_id=50256
    )

    return result[0]['generated_text']

# Gradio UI definition
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."
)

# Launch the app
demo.launch()