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import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
import spacy
# Load models
model = GPT2LMHeadModel.from_pretrained("./")
tokenizer = GPT2Tokenizer.from_pretrained("./")
tokenizer.pad_token = tokenizer.eos_token
try:
nlp = spacy.load("en_core_web_sm")
except OSError:
import spacy.cli
spacy.cli.download("en_core_web_sm")
nlp = spacy.load("en_core_web_sm")
def summarize_description(text):
doc = nlp(text)
keywords = [token.text for token in doc if token.pos_ in ["NOUN", "PROPN", "ADJ"]]
return " ".join(keywords[:12])
def generate_slogans(brand, description, industry, tone="playful", num=5, liked_slogan=None):
processed_desc = summarize_description(description)
if liked_slogan:
prompt1 = (
f"Create {industry} brand slogans similar to: '{liked_slogan}'\n"
f"Brand: {brand}\n"
f"Key Attributes: {processed_desc}\n"
"Slogan:"
)
prompt2 = (
f"Generate slogans in the style of: '{liked_slogan}'\n"
f"For: {brand}\n"
f"Details: {processed_desc}\n"
"Slogan:"
)
else:
prompt1 = (
f"Create a {industry} brand slogan that's {tone} and unique.\n"
f"Brand: {brand}\n"
f"Attributes: {processed_desc}\n"
"Slogan:"
)
prompt2 = (
f"Write {tone} marketing slogans for this {industry} brand:\n"
f"Name: {brand}\n"
f"About: {processed_desc}\n"
"Slogan:"
)
tone_presets = {
"playful": {"temperature": 0.95, "top_p": 0.95, "repetition_penalty": 1.2},
"bold": {"temperature": 0.8, "top_p": 0.9, "repetition_penalty": 1.45},
"minimalist": {"temperature": 0.6, "top_p": 0.8, "repetition_penalty": 1.5},
"luxury": {"temperature": 0.7, "top_p": 0.85, "repetition_penalty": 1.35},
"classic": {"temperature": 0.7, "top_p": 0.9, "repetition_penalty": 1.25}
}
gen_params = {
**tone_presets[tone],
"max_new_tokens": 25,
"num_return_sequences": num,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id
}
# Generate from both prompts
outputs1 = model.generate(**tokenizer(prompt1, return_tensors="pt"), **gen_params)
outputs2 = model.generate(**tokenizer(prompt2, return_tensors="pt"), **gen_params)
slogans = []
for outputs in [outputs1, outputs2]:
for output in outputs:
raw = tokenizer.decode(output, skip_special_tokens=True)
clean = raw.split("Slogan:")[-1].strip()
clean = clean.split("\n")[0].replace('"', '').replace('(', '').split(".")[0].strip()
if len(clean) > 4 and clean not in slogans:
slogans.append(clean)
return {"slogans": slogans[:num * 2]}
# Gradio interface
inputs = [
gr.Textbox(label="Brand"),
gr.Textbox(label="Description"),
gr.Textbox(label="Industry"),
gr.Dropdown(["playful", "bold", "minimalist", "luxury", "classic"], label="Tone", value="playful"),
gr.Slider(1, 10, step=1, value=5, label="Number of Slogans"),
gr.Textbox(label="Generate like this slogan (optional)", value=None)
]
outputs = gr.JSON(label="Generated Slogans")
interface = gr.Interface(
fn=generate_slogans,
inputs=inputs,
outputs=outputs,
title="Slogan Generator API",
flagging_mode="never"
# allow_flagging="never"
)
# Launch with API endpoint
interface.launch(show_api=True)
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