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| """ | |
| Enterprise Creative Campaign Suite - Separate-section generation (FLAN-T5-XL) | |
| Author: Assistant (adapted for your needs) | |
| Notes: | |
| - This script calls the HF pipeline 5 times (one per section). | |
| - If you have GPU, set device=0 inside the pipeline(...) call for speed. | |
| """ | |
| import tempfile | |
| from fpdf import FPDF | |
| import gradio as gr | |
| from transformers import pipeline | |
| # ----------------------------- | |
| # MODEL SETUP - FLAN-T5-XL | |
| # ----------------------------- | |
| # If you have GPU, set device=0 | |
| generator = pipeline( | |
| "text2text-generation", | |
| model="google/flan-t5-xl", | |
| # device=0, # uncomment if you want to use the first CUDA GPU | |
| ) | |
| # ----------------------------- | |
| # Brand Memory (simple key-value) | |
| # ----------------------------- | |
| brand_memory = { | |
| "tone": "Professional, modern, emotionally resonant", | |
| "style": "Story-driven, cinematic, customer-first", | |
| } | |
| # ----------------------------- | |
| # Low-level wrapper to call model | |
| # ----------------------------- | |
| def gen_one(prompt: str, max_new_tokens: int = 220, temperature: float = 0.8, do_sample: bool = True): | |
| """Call the HF pipeline and return generated text (stripped).""" | |
| out = generator(prompt, max_new_tokens=max_new_tokens, do_sample=do_sample, temperature=temperature) | |
| # Pipeline returns a list of dicts; take first | |
| return out[0]["generated_text"].strip() | |
| # ----------------------------- | |
| # Per-section prompt builders | |
| # ----------------------------- | |
| def build_creative_direction_prompt(brand, product, goal, tone, audience, region): | |
| return f"""You are an expert creative strategist. Write a **3–5 line Creative Direction** for a premium brand campaign. | |
| Brand: {brand} | |
| Product: {product} | |
| Goal: {goal} | |
| Tone: {tone}. Brand historically sounds like: {brand_memory.get('tone')} | |
| Audience: {audience} | |
| Region: {region} | |
| Constraints: | |
| - Output ONLY the creative direction text (3-5 lines). | |
| - No headings, no extra commentary. | |
| - Make it cinematic, emotionally resonant, and brand-forward. | |
| """ | |
| def build_emotional_tone_prompt(brand, product, goal, tone, audience, region): | |
| return f"""Write the Emotional Tone of the campaign in 4-6 lines. | |
| Brand: {brand} | |
| Product: {product} | |
| Goal: {goal} | |
| Tone: {tone}. Brand memory: {brand_memory.get('tone')} | |
| Audience: {audience} | |
| Region: {region} | |
| Explain: emotional personality, core feelings to evoke, voice, and how it should make the audience feel. | |
| Output ONLY the emotional tone paragraph(s). No headings. | |
| """ | |
| def build_ad_script_prompt(brand, product, goal, tone, audience, region, script_style="Hybrid"): | |
| """ | |
| Hybrid = cinematic storytelling + marketing clarity. | |
| We'll ask for labeled sections inside the script: Intro, Body, CTA. | |
| """ | |
| return f"""Write a cinematic + marketing hybrid Ad Script for a 30-60 second film ad. Include labeled sections: "Intro:", "Body:", "CTA:". | |
| Brand: {brand} | |
| Product: {product} | |
| Goal: {goal} | |
| Tone: {tone}. Brand memory: {brand_memory.get('tone')} | |
| Audience: {audience} | |
| Region: {region} | |
| Style: Hybrid (mix cinematic imagery and short marketing beats). Keep each labeled section concise (Intro: 2-4 lines, Body: 6-10 lines, CTA: 1-2 lines). Use vivid sensory details and short punch lines for the CTA. | |
| Output only the script text with the labeled sections (Intro:, Body:, CTA:). No extra headings or commentary. | |
| """ | |
| def build_poster_prompt_prompt(brand, product, goal, tone, audience, region): | |
| return f"""Generate a single-paragraph Stable Diffusion / SDXL poster prompt for the campaign. | |
| Brand: {brand} | |
| Product: {product} | |
| Goal: {goal} | |
| Tone: {tone}. Brand memory: {brand_memory.get('tone')} | |
| Audience: {audience} | |
| Region: {region} | |
| Include: lighting, color palette, environment, camera angle, characters (appearance, clothing, pose), props, mood, depth of field suggestions, and where to place typography. | |
| Output only one descriptive paragraph (no headings). | |
| """ | |
| def build_taglines_prompt(brand, product, goal, tone, audience, region): | |
| return f"""Write **two** powerful taglines for the campaign. Keep them short, memorable, and emotionally resonant. | |
| Brand: {brand} | |
| Product: {product} | |
| Goal: {goal} | |
| Tone: {tone}. Brand memory: {brand_memory.get('tone')} | |
| Audience: {audience} | |
| Region: {region} | |
| Format (exact): | |
| - <Tagline 1> | |
| - <Tagline 2> | |
| Output only the two lines prefixed with a dash as shown. | |
| """ | |
| # ----------------------------- | |
| # Combined generator that calls the model for each section | |
| # ----------------------------- | |
| def generate_campaign(brand_name, product_type, goal, tone, audience, region, temperature=0.8, do_sample=True): | |
| """ | |
| Calls the model separately for each section and returns assembled markdown plus raw sections. | |
| """ | |
| # Ensure brand memory hint appended to tone | |
| tone_hint = f"{tone}. The brand historically sounds like: {brand_memory.get('tone')}" | |
| # Build prompts | |
| p_cd = build_creative_direction_prompt(brand_name, product_type, goal, tone_hint, audience, region) | |
| p_et = build_emotional_tone_prompt(brand_name, product_type, goal, tone_hint, audience, region) | |
| p_ad = build_ad_script_prompt(brand_name, product_type, goal, tone_hint, audience, region, script_style="Hybrid") | |
| p_pp = build_poster_prompt_prompt(brand_name, product_type, goal, tone_hint, audience, region) | |
| p_tg = build_taglines_prompt(brand_name, product_type, goal, tone_hint, audience, region) | |
| # Generate each section (separate calls) | |
| creative_direction = gen_one(p_cd, max_new_tokens=200, temperature=temperature, do_sample=do_sample) | |
| emotional_tone = gen_one(p_et, max_new_tokens=180, temperature=temperature, do_sample=do_sample) | |
| ad_script = gen_one(p_ad, max_new_tokens=420, temperature=temperature, do_sample=do_sample) | |
| poster_prompt = gen_one(p_pp, max_new_tokens=200, temperature=temperature, do_sample=do_sample) | |
| taglines = gen_one(p_tg, max_new_tokens=80, temperature=temperature, do_sample=do_sample) | |
| # Assemble markdown | |
| md = f""" | |
| # 🎨 **Enterprise Creative Campaign Output** | |
| --- | |
| ## ✨ Brand: **{brand_name}** | |
| ### 🌍 Region: {region} | |
| --- | |
| ## 🎯 **Creative Direction** | |
| {creative_direction} | |
| --- | |
| ## ❤️ **Emotional Tone** | |
| {emotional_tone} | |
| --- | |
| ## 🎬 **Ad Script** | |
| {ad_script} | |
| --- | |
| ## 🖼 **Poster Prompt** | |
| {poster_prompt} | |
| --- | |
| ## 🔥 **Taglines** | |
| {taglines} | |
| --- | |
| """ | |
| # Return markdown plus each piece so UI can store/use them | |
| return md, creative_direction, emotional_tone, ad_script, poster_prompt, taglines | |
| # ----------------------------- | |
| # Poster prompt single-call helper (tab 2) | |
| # ----------------------------- | |
| def generate_poster_prompt(brand, mood, theme, audience, temperature=0.8, do_sample=True): | |
| prompt = f"""Create a cinematic AI poster prompt for Stable Diffusion / SDXL. | |
| Brand: {brand} | |
| Mood: {mood} | |
| Theme: {theme} | |
| Audience: {audience} | |
| Brand memory: {brand_memory.get('tone')} | |
| OUTPUT: Single paragraph describing lighting, color palette, environment, camera angle, people (appearance, clothing, poses), props, depth of field, mood, and typography placement. | |
| """ | |
| return gen_one(prompt, max_new_tokens=240, temperature=temperature, do_sample=do_sample) | |
| # ----------------------------- | |
| # Update Brand Memory | |
| # ----------------------------- | |
| def update_brand_memory(new_tone, new_style): | |
| if new_tone: | |
| brand_memory["tone"] = new_tone | |
| if new_style: | |
| brand_memory["style"] = new_style | |
| return "✅ Brand memory updated successfully!", str(brand_memory) | |
| # ----------------------------- | |
| # Export to PDF | |
| # ----------------------------- | |
| def export_pdf(text): | |
| pdf = FPDF() | |
| pdf.set_auto_page_break(auto=True, margin=15) | |
| pdf.add_page() | |
| pdf.set_font("Arial", size=12) | |
| for line in text.split("\n"): | |
| pdf.multi_cell(0, 7, line) | |
| tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") | |
| pdf.output(tmp.name) | |
| return tmp.name | |
| # ----------------------------- | |
| # Gradio UI | |
| # ----------------------------- | |
| with gr.Blocks(theme=gr.themes.Monochrome()) as demo: | |
| gr.HTML(""" | |
| <div style='text-align:center; padding:20px'> | |
| <h1 style='font-size:40px;'>🚀 Enterprise Creative Campaign Suite</h1> | |
| <p style='font-size:18px; color:#ddd;'>FLAN-T5-XL • Sectioned generation (Hybrid script)</p> | |
| </div> | |
| """) | |
| with gr.Tabs(): | |
| # Campaign Generator Tab | |
| with gr.Tab("🎨 Campaign Generator"): | |
| with gr.Row(): | |
| brand_name = gr.Textbox(label="Brand Name", value="Chessy") | |
| product_type = gr.Textbox(label="Product Type", value="Premium Chess Set") | |
| goal = gr.Textbox(label="Campaign Goal", value="Increase D2C sales by 30% in 3 months") | |
| tone = gr.Textbox(label="Tone / Voice", value="Warm, aspirational, cinematic") | |
| audience = gr.Textbox(label="Target Audience", value="Young professionals, 25-40, India") | |
| region = gr.Textbox(label="Region", value="India") | |
| # Sampling controls | |
| with gr.Row(): | |
| temperature = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.8, label="Temperature") | |
| sampling_toggle = gr.Checkbox(value=True, label="Use sampling (do_sample)") | |
| run_btn = gr.Button("🚀 Generate Full Campaign", variant="primary") | |
| output_md = gr.Markdown("Your campaign will appear here…", elem_id="campaign_output_md") | |
| # Hidden outputs for each section | |
| cd = gr.Textbox(visible=False) | |
| et = gr.Textbox(visible=False) | |
| ad = gr.Textbox(visible=False) | |
| pp = gr.Textbox(visible=False) | |
| tg = gr.Textbox(visible=False) | |
| run_btn.click( | |
| fn=generate_campaign, | |
| inputs=[brand_name, product_type, goal, tone, audience, region, temperature, sampling_toggle], | |
| outputs=[output_md, cd, et, ad, pp, tg] | |
| ) | |
| # Poster Prompt Tab | |
| with gr.Tab("🖼 Poster Prompt Generator"): | |
| brandp = gr.Textbox(label="Brand Name", value="Chessy") | |
| mood = gr.Textbox(label="Mood & Emotion", value="Epic, reflective") | |
| themep = gr.Textbox(label="Theme", value="Master vs Newcomer") | |
| audp = gr.Textbox(label="Audience", value="Players & collectors") | |
| # sampling controls local for poster | |
| poster_temp = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.8, label="Temperature (poster)") | |
| poster_sample = gr.Checkbox(value=True, label="Use sampling for poster") | |
| gen_poster_btn = gr.Button("🎨 Generate Poster Prompt") | |
| poster_out = gr.Textbox(label="Poster Prompt", lines=8) | |
| gen_poster_btn.click( | |
| generate_poster_prompt, | |
| [brandp, mood, themep, audp, poster_temp, poster_sample], | |
| poster_out | |
| ) | |
| # Brand Memory Tab | |
| with gr.Tab("🧠 Brand Tone Memory"): | |
| gr.Markdown("### Update your persistent brand tone + style memory") | |
| new_tone = gr.Textbox(label="New Tone") | |
| new_style = gr.Textbox(label="New Style") | |
| upd_btn = gr.Button("💾 Update Memory") | |
| msg = gr.Textbox(label="Status") | |
| mem = gr.Textbox(label="Current Memory", lines=5, value=str(brand_memory)) | |
| upd_btn.click(update_brand_memory, [new_tone, new_style], [msg, mem]) | |
| # Export Center Tab | |
| with gr.Tab("📦 Export Center"): | |
| gr.Markdown("### Export your full campaign as PDF") | |
| export_btn = gr.Button("📄 Export Campaign as PDF") | |
| export_file = gr.File() | |
| export_btn.click(export_pdf, inputs=output_md, outputs=export_file) | |
| demo.launch() |