Update app.py

app.py

@@ -1,19 +1,24 @@
-import re
+"""
+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: FLAN-T5-XL
+# MODEL SETUP - FLAN-T5-XL
 # -----------------------------
-# Keep Flan-T5-XL as requested. If you want to run on CPU-only, remove device arg.
-# On systems with GPU change device to 0 (cuda) for speed.
+# If you have GPU, set device=0
 generator = pipeline(
     "text2text-generation",
     model="google/flan-t5-xl",
-    # device=0,  # uncomment if running on a GPU
+    # device=0,  # uncomment if you want to use the first CUDA GPU
 )
 
 # -----------------------------
@@ -25,115 +30,122 @@ brand_memory = {
 }
 
 # -----------------------------
-# Helper: Robust extractor
+# Low-level wrapper to call model
 # -----------------------------
-def extract_section(section_name: str, text: str) -> str:
-    """
-    Extract section block that follows a header like:
-    Section Name:
-    <content possibly multiline>
-
-    Uses a DOTALL regex and anchors looking for the next header line that
-    starts with a capitalized word and colon.
-    """
-    # Ensure we match the exact header plus colon, allow optional whitespace
-    pattern = rf"^{re.escape(section_name)}:\s*(.*?)(?=^\w[\w\d \-']+:\s*$|$\Z)"
-    match = re.search(pattern, text, flags=re.MULTILINE | re.DOTALL | re.IGNORECASE)
-    if match:
-        return match.group(1).strip()
-    return "Not found."
+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()
 
 # -----------------------------
-# Prompt template + fallback wrapper
+# Per-section prompt builders
 # -----------------------------
-BASE_OUTPUT_FORMAT = """
-OUTPUT FORMAT (FOLLOW EXACTLY):
+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.
 
-Creative Direction:
-<3–5 line vision>
+Brand: {brand}
+Product: {product}
+Goal: {goal}
+Tone: {tone}. Brand historically sounds like: {brand_memory.get('tone')}
+Audience: {audience}
+Region: {region}
 
-Emotional Tone:
-<deep emotional personality + brand resonance>
+Constraints:
+- Output ONLY the creative direction text (3-5 lines).
+- No headings, no extra commentary.
+- Make it cinematic, emotionally resonant, and brand-forward.
+"""
 
-Ad Script:
-<Cinematic script with labeled Intro, Body, CTA>
+def build_emotional_tone_prompt(brand, product, goal, tone, audience, region):
+    return f"""Write the Emotional Tone of the campaign in 4-6 lines.
 
-Poster Prompt:
-<Highly visual Stable Diffusion / SDXL style prompt describing lighting, color palette, environment, camera angle, people, clothing, props, mood, and typography>
+Brand: {brand}
+Product: {product}
+Goal: {goal}
+Tone: {tone}. Brand memory: {brand_memory.get('tone')}
+Audience: {audience}
+Region: {region}
 
-Taglines:
-- <Tagline 1>
-- <Tagline 2>
+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 call_generator_with_format(prompt_text, max_new_tokens=650, temperature=0.8, retry_if_bad=True):
+def build_ad_script_prompt(brand, product, goal, tone, audience, region, script_style="Hybrid"):
     """
-    Calls the HF pipeline and ensures output contains the required section headers.
-    If not, does one lightweight re-prompt to ask the model to reformat the previous answer
-    strictly following the OUTPUT FORMAT. (Helps Flan-T5-XL follow strict formatting.)
+    Hybrid = cinematic storytelling + marketing clarity.
+    We'll ask for labeled sections inside the script: Intro, Body, CTA.
     """
-    full_response = generator(prompt_text, max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature)[0]["generated_text"]
-
-    # quick check: does it contain the main headers?
-    required_headers = ["Creative Direction:", "Emotional Tone:", "Ad Script:", "Poster Prompt:", "Taglines:"]
-    if all(h.lower() in full_response.lower() for h in required_headers):
-        return full_response
-
-    if retry_if_bad:
-        # concise reformat request appended to previous model output
-        reformat_prompt = (
-            full_response
-            + "\n\nThe above answer is good, but you MUST re-output it following the exact OUTPUT FORMAT below. "
-            + BASE_OUTPUT_FORMAT
-            + "\n\nReformat now and include the same content but strictly follow the headings and colons."
-        )
-        full_response = generator(reformat_prompt, max_new_tokens=500, do_sample=False, temperature=0.0)[0]["generated_text"]
-    return full_response
+    return f"""Write a cinematic + marketing hybrid Ad Script for a 30-60 second film ad. Include labeled sections: "Intro:", "Body:", "CTA:".
 
-# -----------------------------
-# Core Campaign Generator
-# -----------------------------
-def generate_campaign(brand_name, product_type, goal, tone, audience, region):
-    # Compose combined tone with brand memory hint
-    combined_tone = f"{tone}. The brand historically sounds like this: {brand_memory.get('tone')}."
-
-    prompt = f"""
-You are an expert creative strategist hired to design a world-class campaign for a premium brand.
-Input details:
-Brand Name: {brand_name}
-Product Type: {product_type}
+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: {combined_tone}
-Target Audience: {audience}
+Tone: {tone}. Brand memory: {brand_memory.get('tone')}
+Audience: {audience}
 Region: {region}
 
-{BASE_OUTPUT_FORMAT}
+Format (exact):
+- <Tagline 1>
+- <Tagline 2>
 
-Write full content under each heading only (do NOT include extra commentary).
+Output only the two lines prefixed with a dash as shown.
 """
-    llm_output = call_generator_with_format(prompt)
-
-    # Extract sections
-    creative_direction = extract_section("Creative Direction", llm_output)
-    emotional_tone = extract_section("Emotional Tone", llm_output)
-    ad_script = extract_section("Ad Script", llm_output)
-    poster_prompt = extract_section("Poster Prompt", llm_output)
-    taglines = extract_section("Taglines", llm_output)
-
-    # If extractor failed for any section, give fallback message with raw output for debugging
-    # (This helps users see what the model actually returned.)
-    fallback_debug = ""
-    for name, value in [
-        ("Creative Direction", creative_direction),
-        ("Emotional Tone", emotional_tone),
-        ("Ad Script", ad_script),
-        ("Poster Prompt", poster_prompt),
-        ("Taglines", taglines),
-    ]:
-        if value == "Not found.":
-            fallback_debug += f"\n---\nSection '{name}' not parsed. Raw model output was:\n{llm_output}\n"
-
-    # Markdown to display in Gradio
+
+# -----------------------------
+# 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**
 ---
@@ -156,27 +168,24 @@ Write full content under each heading only (do NOT include extra commentary).
 {taglines}
 ---
 """
-    if fallback_debug:
-        md += "\n\n**Parsing debug:** The model output didn't strictly match expected headings for at least one section. See raw output below.\n\n```\n" + llm_output[:4000] + ("\n... (truncated)\n" if len(llm_output) > 4000 else "\n") + "```\n"
-
+    # Return markdown plus each piece so UI can store/use them
     return md, creative_direction, emotional_tone, ad_script, poster_prompt, taglines
 
 # -----------------------------
-# Poster prompt generator (single-purpose)
+# Poster prompt single-call helper (tab 2)
 # -----------------------------
-def generate_poster_prompt(brand, mood, theme, audience):
-    prompt = f"""
-Create a cinematic AI poster prompt for use with Stable Diffusion / SDXL.
+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, no headings):
-Describe lighting, color palette, environment, camera angle, people (appearance, clothing, poses), props, depth of field, mood, and recommended typography placement. Keep it compact but highly descriptive.
+OUTPUT: Single paragraph describing lighting, color palette, environment, camera angle, people (appearance, clothing, poses), props, depth of field, mood, and typography placement.
 """
-    result = generator(prompt, max_new_tokens=300, do_sample=True, temperature=0.8)[0]["generated_text"]
-    return result
+    return gen_one(prompt, max_new_tokens=240, temperature=temperature, do_sample=do_sample)
 
 # -----------------------------
 # Update Brand Memory
@@ -186,22 +195,18 @@ def update_brand_memory(new_tone, new_style):
         brand_memory["tone"] = new_tone
     if new_style:
         brand_memory["style"] = new_style
-    # Return status + memory snapshot
     return "✅ Brand memory updated successfully!", str(brand_memory)
 
 # -----------------------------
-# Export as PDF (keeps the multi-line layout)
+# 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)
-
-    # split by lines and write using multi_cell to handle wrapping
     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
@@ -213,7 +218,7 @@ 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;'>Powered by HuggingFace + Rookus Reasoning Core</p>
+        <p style='font-size:18px; color:#ddd;'>FLAN-T5-XL • Sectioned generation (Hybrid script)</p>
     </div>
     """)
 
@@ -223,16 +228,20 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
             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…")
+            output_md = gr.Markdown("Your campaign will appear here…", elem_id="campaign_output_md")
 
-            # Hidden textboxes to store raw pieces for later export/use
+            # Hidden outputs for each section
             cd = gr.Textbox(visible=False)
             et = gr.Textbox(visible=False)
             ad = gr.Textbox(visible=False)
@@ -241,7 +250,7 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
 
             run_btn.click(
                 fn=generate_campaign,
-                inputs=[brand_name, product_type, goal, tone, audience, region],
+                inputs=[brand_name, product_type, goal, tone, audience, region, temperature, sampling_toggle],
                 outputs=[output_md, cd, et, ad, pp, tg]
             )
 
@@ -251,12 +260,15 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
             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],
+                [brandp, mood, themep, audp, poster_temp, poster_sample],
                 poster_out
             )
 
@@ -275,7 +287,7 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
             gr.Markdown("### Export your full campaign as PDF")
             export_btn = gr.Button("📄 Export Campaign as PDF")
             export_file = gr.File()
-            # When exporting, use the Markdown content (output_md)
+
             export_btn.click(export_pdf, inputs=output_md, outputs=export_file)
 
 demo.launch()