Update app.py

app.py

@@ -10,6 +10,7 @@ import random
 css = """
 body {
     font-family: 'Poppins', -apple-system, BlinkMacSystemFont, sans-serif;
+    #background-color: #fafafa;
 }
 .container {
     max-width: 900px;
@@ -32,9 +33,6 @@ body {
     margin-top: 15px;
     font-family: 'Courier New', monospace;
     white-space: pre-wrap;
-    #border-radius: 8px;
-    #background-color: #f9f9f9;
-    #border: 1px solid #eee;
 }
 footer {
     text-align: center;
@@ -47,8 +45,6 @@ footer {
 .api-key-container {
     padding: 20px;
     margin-bottom: 25px;
-    #background-color: #f5f5f5;
-    #border-radius: 8px;
 }
 .prompt-container {
     margin-bottom: 20px;
@@ -68,9 +64,13 @@ footer {
 }
 .examples-container {
     margin-top: 15px;
-    padding: 15px;
-    #border-radius: 8px;
-    #background-color: #f0f0f0;
+    padding: 10px;
+    border-radius: 8px;
+    background-color: #f0f0f0;
+}
+.tab-content {
+    padding: 20px;
+    background-color: #fff;
 }
 .logo {
     text-align: center;
@@ -80,561 +80,361 @@ footer {
     max-width: 80px;
     height: auto;
 }
+.tabs-container {
+    margin-top: 20px;
+}
 .style-options {
     margin-top: 15px;
-    padding: 15px;
-    #border-radius: 8px;
-    #background-color: #f5f5f5;
 }
 .market-box {
     margin-top: 15px;
     padding: 15px;
-    #border: 1px solid #ddd;
-    #border-radius: 8px;
-    #background-color: #f9f9f9;
+    border: 1px solid #ddd;
+    border-radius: 8px;
+    background-color: #f9f9f9;
 }
 .keyword-list {
     margin-top: 5px;
     font-family: 'Courier New', monospace;
 }
-.style-cards {
-    display: grid;
-    grid-template-columns: repeat(auto-fill, minmax(200px, 1fr));
-    gap: 15px;
-    margin-top: 20px;
-}
-.style-card {
-    #border: 1px solid #ddd;
-    #border-radius: 8px;
-    padding: 15px;
-    background-color: white;
-    transition: transform 0.2s, box-shadow 0.2s;
-}
-.style-card:hover {
-    transform: translateY(-3px);
-    box-shadow: 0 5px 15px rgba(0,0,0,0.1);
-}
-.style-card.selected {
-    border: 2px solid #e94560;
-    background-color: #fff5f7;
-}
-.style-title {
-    font-weight: bold;
-    margin-bottom: 5px;
-    color: #333;
-}
-.style-desc {
-    font-size: 0.9em;
-    color: #666;
-}
-.pod-info {
-    display: flex;
-    margin-top: 10px;
-    font-size: 0.8em;
-}
-.pod-colors, .pod-products {
-    flex: 1;
-    color: #000;
-}
-.pod-colors span, .pod-products span {
-    background: #f0f0f0;
-    padding: 2px 6px;
-    border-radius: 4px;
-    margin-right: 4px;
-    white-space: nowrap;
-    display: inline-block;
-    margin-bottom: 4px;
-    color: #000;
-}
-.analytics-section {
-    margin-top: 20px;
-    padding: 15px;
-    #border-radius: 8px;
-    background-color: #f0f8ff;
-    #border: 1px solid #d0e0ff;
-}
 """
 
-# Define POD styles
-POD_STYLES = {
-    "Auto-Detect": {
-        "guidance": "AI will determine the best style based on quote analysis",
-        "colors": ["Adaptive Colors"],
-        "products": ["Various Products"]
-    },
-    "Streetwear_Urban": {
-        "guidance": "Focus on bold, urban streetwear typography with graffiti influences, distressed elements, and street culture aesthetics. Perfect for hoodies and t-shirts targeting youth culture.",
-        "colors": ["Black", "White", "Bold Accents"],
-        "products": ["Hoodies", "T-shirts", "Snapbacks"]
-    },
-    "Vintage_Retro": {
-        "guidance": "Focus on nostalgic vintage typography with distressed textures, retro fonts, and classic elements from the 50s-90s era. Include aged effects and throwback aesthetics.",
-        "colors": ["Faded", "Weathered", "Retro Palettes"],
-        "products": ["T-shirts", "Hoodies", "Tanks"]
-    },
-    "Minimalist_Clean": {
-        "guidance": "Focus on clean, minimal typography with ample white space, elegant simple fonts, and refined layouts. Prioritize readability and sophistication with subtle design elements.",
-        "colors": ["Black", "White", "Grey", "Neutrals"],
-        "products": ["Premium T-shirts", "Hoodies", "Long Sleeves"]
-    },
-    "Motivational_Fitness": {
-        "guidance": "Focus on dynamic, energetic typography for fitness and motivational quotes. Use strong, powerful fonts with dynamic layouts that convey strength, movement and determination.",
-        "colors": ["Bold", "High-contrast", "Energetic"],
-        "products": ["Workout Tees", "Gym Hoodies", "Tanks"]
-    },
-    "Family_Sentimental": {
-        "guidance": "Focus on heartfelt, emotional typography designs for family quotes. Use warm, inviting fonts with decorative elements like hearts, flourishes, and family-oriented symbols.",
-        "colors": ["Warm", "Comforting", "Heartfelt"],
-        "products": ["Family T-shirts", "Gift Hoodies"]
-    },
-    "Funny_Humor": {
-        "guidance": "Focus on playful, humorous typography with comic-style fonts, fun layouts, and witty visual elements that enhance the humorous quote. Include playful decorations.",
-        "colors": ["Bright", "Playful", "Attention-grabbing"],
-        "products": ["Novelty Tees", "Humorous Hoodies"]
-    },
-}
-
-# Function to generate prompts using Gemini API
-def generate_typography_prompts(api_key, quote, num_prompts=10, style_preference=None):
-    # Configure the Gemini API
+# MODIFIED FUNCTION: Generate structured POD marketing content
+def generate_pod_structured_output(api_key, quote, num_design_prompts, ui_selected_style_name="None (Let AI decide)"):
     genai.configure(api_key=api_key)
     
-    # Create a model
     model = genai.GenerativeModel(
         model_name="gemini-1.5-pro",
         generation_config={
-            "temperature": 0.9,
+            "temperature": 0.85, # Slightly higher for more variety in more prompts
             "top_p": 0.95,
             "top_k": 40,
-            "max_output_tokens": 4000,
+            "max_output_tokens": 3072, # Increased slightly for potentially more prompts
         }
     )
     
-    # Style guidance based on preference
-    style_guidance = ""
-    if style_preference and style_preference in POD_STYLES:
-        style_guidance = POD_STYLES[style_preference]["guidance"]
-    
-    # Craft a detailed prompt for Gemini
-    system_prompt = f"""You are a creative typography and print-on-demand design expert.
-    
-Create {num_prompts} unique, creative typography design prompts for the following quote:
-"{quote}"
+    style_for_header_tag = "AUTO"
+    style_guidance_for_llm = "AI chooses the best-fitting style automatically"
 
-Each prompt should be specifically optimized for print-on-demand products like t-shirts and hoodies.
-Include these elements in each prompt:
-1. A creative, visually striking typography design description
-2. Specific design details (fonts, layout, decorative elements, textures)
-3. POD-specific considerations (placement on garment, sizing, print technique hints)
-4. "Target Audience:" section naming the specific demographic this design would appeal to
-5. "Best Products:" section suggesting 2-3 specific POD products this design works best on
+    if ui_selected_style_name and ui_selected_style_name != "None (Let AI decide)":
+        style_for_header_tag = ui_selected_style_name
+        style_guidance_for_llm = f"The user has selected a preferred style: '{ui_selected_style_name}'. You MUST apply this style to all relevant prompts in Section 4."
+    else:
+        style_guidance_for_llm = "The user has left the style choice empty. AI should choose the best-fitting style automatically for Section 4."
 
-{style_guidance}
+    # The 'num_design_prompts' now dictates the number of ideas in Section 4
+    system_prompt = f"""You are a smart Print-on-Demand (POD) marketing assistant.
+Your task is to analyze the quote "{quote}" and return FOUR structured sections that help with marketing, audience targeting, and design execution.
 
-Use the following format for each prompt:
-[Detailed typography design description with specific visual details]
-Target Audience: [Specific demographic description]
-Best Products: [2-3 specific POD products this design works best on]
+The system has an OPTIONAL STYLES section.
+{style_guidance_for_llm}
+---
+Return only the following four sections:
 
-Make each prompt distinct and highly creative. Focus on designs that would translate well to print-on-demand garments.
-The style should match both the mood of the quote and the intended POD application.
+📈 1. AMAZON SEARCH KEYWORDS
+Generate a comma-separated list of 15-25 SEO-rich keywords relevant to the quote "{quote}".
+- Mix of broad and niche-specific terms
+- Focus on what real buyers would search for on Amazon
+Example: dog lover shirt, funny pet shirt, dog humor tee, running with dog
 
-Examples based on the quote "rise and grind":
-1. Bold, industrial-style typography with "RISE" stacked above "GRIND" in distressed metal-look lettering. Add gear and mechanical elements around the edges with coffee splatter accents. Place centered on chest for maximum impact, with slightly weathered print effect.
-Target Audience: Entrepreneurial hustlers, coffee enthusiasts, gym-goers with morning routines
-Best Products: Black premium t-shirt, dark heather hoodie, athletic tank top
+---
+📅 2. HOLIDAYS / EVENTS / NICHES
+Identify all relevant holidays, occasions, events, and niche categories the quote "{quote}" fits into. (Provide at least 3-5 items)
+- Be specific and include both evergreen and seasonal contexts
+Example: Dog Lovers, Pet Adoption Month, National Dog Day, Funny Workout Shirts
 
-2. Minimalist sunrise gradient typography where "rise" appears in elegant thin script rising upward, while "and grind" follows in small, clean sans-serif below. Create subtle coffee bean silhouettes integrated into the letter spacing. Positioned slightly higher on chest for a modern look.
-Target Audience: Professional go-getters, minimalist style enthusiasts, coffee shop regulars
-Best Products: Premium fitted t-shirt, lightweight hoodie, long sleeve tee
-"""
+---
+🎯 3. TARGET AUDIENCE
+Describe the ideal customer this quote/design ("{quote}") would appeal to.
+- Include: age range, gender, interests, lifestyle traits, tone preference
+Example: Adults 25–45, pet lovers, dog moms, people with sarcastic or playful humor, casual wearers
 
-    try:
-        # Generate content
-        response = model.generate_content([system_prompt])
-        
-        # Extract the text from the response
-        result = response.text
-        
-        # Post-process to ensure format consistency
-        prompts = []
-        
-        # Split into individual prompts and clean up
-        raw_prompts = re.split(r'\d+\.\s+', result)[1:]  # Skip first empty element
-        
-        if not raw_prompts:  # If splitting didn't work, try another approach
-            raw_prompts = result.split("\n\n")
-            
-        for prompt in raw_prompts:
-            # Clean up the prompt and ensure it has proper structure
-            prompt = prompt.strip()
-            if not prompt:
-                continue
-                
-            # Ensure there's a Target Audience section
-            if "Target Audience:" not in prompt:
-                parts = prompt.split('\n')
-                prompt = parts[0] + "\nTarget Audience: Print-on-demand customers, typography enthusiasts."
-            
-            # Ensure there's a Best Products section
-            if "Best Products:" not in prompt:
-                prompt += "\nBest Products: T-shirt, Hoodie, Long Sleeve Tee"
-                
-            prompts.append(prompt)
-            
-        # If we still don't have enough prompts, try another parsing method
-        if len(prompts) < num_prompts/2:
-            lines = result.split('\n')
-            current_prompt = ""
-            
-            for line in lines:
-                if re.match(r'^\d+\.', line):  # New prompt begins
-                    if current_prompt:
-                        prompts.append(current_prompt.strip())
-                        current_prompt = line[line.find('.')+1:].strip()
-                    else:
-                        current_prompt = line[line.find('.')+1:].strip()
-                else:
-                    current_prompt += "\n" + line
-            
-            if current_prompt:  # Add the last prompt
-                prompts.append(current_prompt.strip())
-        
-        # Ensure we have the right number of prompts
-        prompts = prompts[:num_prompts]
-            
-        return prompts, None
-        
-    except Exception as e:
-        return None, f"Error: {str(e)}"
+---
+🎨 4. PROMPTS ({num_design_prompts} Ideas) — STYLE: [{style_for_header_tag}]
+Generate exactly {num_design_prompts} creative design prompts for the quote: "{quote}", based on:
+- Tone and meaning of the quote
+- The style determined above (either user-selected '{style_for_header_tag if style_for_header_tag != "AUTO" else "AI Choice"}' or AI-recommended if AUTO)
+- Each prompt should include layout, typography style, design elements, and color ideas.
+If a style was provided by the user, apply it consistently.
+If no style was provided (AUTO), analyze the quote and recommend and apply a fitting visual style.
 
-# Generate market keywords for Amazon products
-def generate_market_keywords(api_key, quote):
-    # Configure the Gemini API
-    genai.configure(api_key=api_key)
-    
-    # Create a model
-    model = genai.GenerativeModel(
-        model_name="gemini-1.5-pro",
-        generation_config={
-            "temperature": 0.7,
-            "top_p": 0.95,
-            "max_output_tokens": 1000,
-        }
-    )
-    
-    # Craft a detailed prompt for Gemini
-    system_prompt = f"""Based on the following quote: "{quote}"
+Example (If User Selected Style = Silhouette and num_design_prompts = 1):
+"1. Bold, stacked text in athletic font for THE PROVIDED QUOTE. A silhouette of a dog pulling a leash with a person chasing behind. High contrast black-and-white design optimized for light shirts."
 
-1. Identify up to 3 relevant holidays, events, or niches where this quote would be most marketable as a design on print-on-demand products (t-shirts, hoodies).
+Example (If Style = AUTO, AI chose Retro, and num_design_prompts = 1):
+"1. Retro block lettering in faded orange and teal for THE PROVIDED QUOTE. Text arches above a cartoon dog flexing with sunglasses. Distressed texture for a vintage look."
+---
+⚠️ Output Format Guidelines:
+- Use headers exactly as shown (including the emojis, numbering, and the dynamic prompt count in Section 4 header).
+- For Section 4, number each design prompt idea starting from 1.
+- Do NOT restate the provided quote "{quote}" unless it's within the design prompt itself where it's part of the design concept.
+- Do NOT explain your reasoning outside the requested sections.
+- Keep your output clean and implementation-ready, directly providing the content under each header.
+- Ensure each section is clearly delineated by the headers and newlines.
+"""
+    print("--- SYSTEM PROMPT BEING SENT TO API (First 500 chars) ---")
+    print(system_prompt[:500] + "...") 
+    print("--- END OF SYSTEM PROMPT PREVIEW ---")
 
-2. Generate 15-20 Amazon/Etsy search keywords that potential customers might use to find POD products featuring this quote.
+    try:
+        print(f"[{time.strftime('%Y-%m-%d %H:%M:%S')}] Attempting to generate {num_design_prompts} prompts for quote: '{quote}' with style: '{ui_selected_style_name}'")
+        response = model.generate_content([system_prompt]) 
+        print(f"[{time.strftime('%Y-%m-%d %H:%M:%S')}] Received response from API.")
+        return response.text, None
+    except Exception as e:
+        error_message = f"Error during API call: {str(e)}"
+        print(f"[{time.strftime('%Y-%m-%d %H:%M:%S')}] {error_message}")
+        if hasattr(e, 'message'):
+             error_message = f"API Error: {e.message}"
+        return None, error_message
 
-3. Identify 3-5 specific customer personas who would be most likely to purchase this design.
 
-Format your response as follows:
+# MODIFIED PARSING FUNCTION for the structured output
+def parse_structured_output(text_output):
+    data = {
+        "keywords_header": "📈 1. AMAZON SEARCH KEYWORDS",
+        "keywords_content": "Not found or parsing error.",
+        "holidays_header": "📅 2. HOLIDAYS / EVENTS / NICHES",
+        "holidays_content": "Not found or parsing error.",
+        "audience_header": "🎯 3. TARGET AUDIENCE",
+        "audience_content": "Not found or parsing error.",
+        "prompts_header": "🎨 4. PROMPTS — STYLE: N/A", # Default, will be replaced
+        "prompts_content": "Not found or parsing error."
+    }
 
-HOLIDAYS/EVENTS/NICHES:
-- [Holiday/Event/Niche 1]
-- [Holiday/Event/Niche 2]
-- [Holiday/Event/Niche 3]
+    kw_match = re.search(r"(📈 1\. AMAZON SEARCH KEYWORDS)\s*\n(.*?)(?=\n\n📅 2\. HOLIDAYS / EVENTS / NICHES|\Z)", text_output, re.DOTALL)
+    if kw_match:
+        data["keywords_header"] = kw_match.group(1).strip()
+        data["keywords_content"] = kw_match.group(2).strip()
 
-SEARCH KEYWORDS:
-[Keyword 1], [Keyword 2], [Keyword 3], etc.
+    hol_match = re.search(r"(📅 2\. HOLIDAYS / EVENTS / NICHES)\s*\n(.*?)(?=\n\n🎯 3\. TARGET AUDIENCE|\Z)", text_output, re.DOTALL)
+    if hol_match:
+        data["holidays_header"] = hol_match.group(1).strip()
+        data["holidays_content"] = hol_match.group(2).strip()
 
-CUSTOMER PERSONAS:
-1. [Name] - [Age range] - [Brief description including occupation and interests]
-2. [Name] - [Age range] - [Brief description including occupation and interests]
-3. [Name] - [Age range] - [Brief description including occupation and interests]
+    # Lookahead for prompts section needs to be dynamic or very general
+    aud_match = re.search(r"(🎯 3\. TARGET AUDIENCE)\s*\n(.*?)(?=\n\n🎨 4\. PROMPTS \(\d+ Ideas?\) — STYLE:|\Z)", text_output, re.DOTALL)
+    if aud_match:
+        data["audience_header"] = aud_match.group(1).strip()
+        data["audience_content"] = aud_match.group(2).strip()
+    
+    # Regex for prompts header is now more general to capture the number of ideas
+    # It matches "🎨 4. PROMPTS (N Ideas) — STYLE: [STYLE]"
+    prompt_header_pattern_match = re.search(r"(🎨 4\. PROMPTS \(\d+\s*Ideas?\) — STYLE:.*?)\s*\n(.*?)\Z", text_output, re.DOTALL)
+    if prompt_header_pattern_match:
+        data["prompts_header"] = prompt_header_pattern_match.group(1).strip()
+        data["prompts_content"] = prompt_header_pattern_match.group(2).strip()
+    else: 
+        # Fallback if the "(N Ideas)" part is missing for some reason
+        prompt_fallback_match = re.search(r"(🎨 4\. PROMPTS — STYLE:.*?)\s*\n(.*?)\Z", text_output, re.DOTALL)
+        if prompt_fallback_match:
+            data["prompts_header"] = prompt_fallback_match.group(1).strip()
+            data["prompts_content"] = prompt_fallback_match.group(2).strip()
+            print("Warning: Parsed prompts using fallback regex. Header might not exactly match '(N Ideas)'.")
+            
+    return data
 
-Make sure the keywords are relevant to the quote and would be used by actual shoppers looking for POD products with such messages. Focus on gift-giving occasions, product types, recipient types, and sentiment.
-"""
 
-    try:
-        # Generate content
-        response = model.generate_content([system_prompt])
-        
-        # Extract the text from the response
-        result = response.text
-        
-        return result, None
-        
-    except Exception as e:
-        return None, f"Error: {str(e)}"
-
-# Function to analyze quote and suggest the best style automatically
-def analyze_quote_for_style(api_key, quote):
-    if not api_key or not quote:
-        return "Auto-Detect"  # Default
+# Function to analyze quote sentiment and suggest styles
+def analyze_quote(api_key, quote, progress=gr.Progress()):
+    if not api_key: return "Please enter your Gemini API key for analysis."
+    if not quote: return "Please enter a quote to analyze."
     
-    # Configure the Gemini API
     genai.configure(api_key=api_key)
-    
-    # Create a model
     model = genai.GenerativeModel(
         model_name="gemini-1.5-pro",
-        generation_config={
-            "temperature": 0.3,  # Lower temperature for more deterministic results
-            "top_p": 0.95,
-            "max_output_tokens": 500,
-        }
+        generation_config={"temperature": 0.7, "top_p": 0.95, "max_output_tokens": 1000}
     )
-    
-    # Get the style names without Auto-Detect
-    style_options = list(POD_STYLES.keys())
-    if "Auto-Detect" in style_options:
-        style_options.remove("Auto-Detect")
-    
-    # Craft a detailed prompt for Gemini
-    system_prompt = f"""Analyze the following quote for a t-shirt/hoodie design:
-"{quote}"
-
-Based on the quote's theme, tone, and content, select the MOST appropriate style category from this list:
-{', '.join(style_options)}
-
-Return only the exact name of the most appropriate style from the list, nothing else.
-"""
-
+    system_prompt = f"""Analyze the following quote: "{quote}"
+Provide a brief analysis of:
+1. The overall mood and sentiment of the quote
+2. The most appropriate typography styles for this quote
+3. Suggested color palettes that would complement the meaning
+4. Best type of decorative elements to include
+Format your response as a concise paragraph for each point."""
     try:
-        # Generate content
         response = model.generate_content([system_prompt])
-        
-        # Extract the text from the response
-        result = response.text.strip()
-        
-        # Ensure the result matches one of our styles
-        for style in style_options:
-            if style.lower() in result.lower():
-                return style
-        
-        # Default to the first style if no match
-        return style_options[0]
-        
+        return response.text
     except Exception as e:
-        return "Streetwear_Urban"  # Default to first style if error
+        return f"Error analyzing quote: {str(e)}"
 
-# Function to handle form submission
-def process_quote(api_key, quote, num_prompts, style_preference, progress=gr.Progress()):
-    if not api_key:
-        return "Please enter your Gemini API key.", "", ""
+# MODIFIED Function to handle form submission
+def process_quote(api_key, quote, num_prompts_from_slider, ui_style_preference_str, progress=gr.Progress()):
+    if not api_key: return "Please enter your Gemini API key.", "API key is required."
+    if not quote: return "Please enter a quote.", "A quote is required."
     
-    if not quote:
-        return "Please enter a quote to generate typography prompts.", "", ""
-    
-    # Show progress and add small delays for better user experience
+    # Ensure num_prompts_from_slider is an int for the system prompt
+    try:
+        num_design_prompts = int(num_prompts_from_slider)
+    except ValueError:
+        return "Invalid number of prompts selected.", "Error with prompt count."
+
     progress(0, desc="Initializing...")
-    time.sleep(0.3)
+    time.sleep(0.1)
     
-    progress(0.2, desc="Connecting to Gemini API...")
-    time.sleep(0.3)
+    progress(0.2, desc=f"Generating {num_design_prompts} design prompts & marketing content...")
     
-    # Auto-detect style if needed
-    detected_style = style_preference
-    if style_preference == "Auto-Detect":
-        progress(0.3, desc="Analyzing quote for best style...")
-        detected_style = analyze_quote_for_style(api_key, quote)
-        auto_detect_info = f"Based on quote analysis, the AI selected the '{detected_style}' style."
-    else:
-        auto_detect_info = ""
-    
-    progress(0.5, desc="Generating creative prompts...")
-    prompts, error = generate_typography_prompts(api_key, quote, num_prompts, detected_style)
+    # Pass num_design_prompts to the generation function
+    raw_llm_output, error = generate_pod_structured_output(api_key, quote, num_design_prompts, ui_style_preference_str)
     
     if error:
-        return error, "", ""
-    
-    progress(0.7, desc="Identifying market opportunities...")
-    market_info, market_error = generate_market_keywords(api_key, quote)
-    
-    if market_error:
-        market_info = "Could not generate market information at this time."
+        return f"Error from API: {error}", f"Marketing insights could not be retrieved. Details: {error}"
+    if not raw_llm_output:
+        return "Received no content from API.", "Received no content for marketing insights from API."
+
+    progress(0.7, desc="Parsing API output...")
+    parsed_data = parse_structured_output(raw_llm_output)
     
-    progress(0.9, desc="Formatting results...")
-    time.sleep(0.3)
+    market_info_text = (
+        f"{parsed_data['keywords_header']}\n{parsed_data['keywords_content']}\n\n"
+        f"{parsed_data['holidays_header']}\n{parsed_data['holidays_content']}\n\n"
+        f"{parsed_data['audience_header']}\n{parsed_data['audience_content']}"
+    )
     
-    # Format the prompts into a nice output
-    result_text = f"Typography Prompts for: \"{quote}\"\n"
-    if auto_detect_info:
-        result_text += f"\n{auto_detect_info}\n"
-    result_text += f"\nStyle: {detected_style}\n\n"
+    prompts_text = f"{parsed_data['prompts_header']}\n{parsed_data['prompts_content']}"
     
-    for i, prompt in enumerate(prompts, 1):
-        result_text += f"-- Prompt {i} --\n{prompt}\n\n"
+    if "Not found or parsing error." in prompts_text and "Not found or parsing error." in market_info_text:
+         error_message = "Could not fully parse the API output. This might indicate an unexpected response format from the AI."
+         raw_output_preview = raw_llm_output[:1000] + "..." if len(raw_llm_output) > 1000 else raw_llm_output
+         prompts_text = error_message + "\n\nRaw API Output (preview):\n" + raw_output_preview
+         market_info_text = "See main output area for details on parsing failure."
     
     progress(1.0, desc="Done!")
-    return result_text, market_info, detected_style
+    return prompts_text, market_info_text
 
-# Example quotes for examples
 example_quotes = [
-    "hustle mode: activated",
-    "mama bear",
-    "weekend vibes",
-    "best dad ever",
-    "sarcastic comment loading",
-    "coffee first, then adulting"
+    "good vibes only", "dream big, sparkle more", "best mom ever",
+    "adventure awaits", "but first, coffee", "fueled by caffeine and chaos",
+    "stay wild moon child"
 ]
 
-# Create style cards for selection
-def create_style_cards():
-    style_cards_html = ""
-    for style_id, style_data in POD_STYLES.items():
-        # Create HTML for each style card
-        colors_html = "".join([f'<span>{color}</span>' for color in style_data["colors"]])
-        products_html = "".join([f'<span>{product}</span>' for product in style_data["products"]])
-        
-        style_cards_html += f"""
-        <div class="style-card" id="style-{style_id}" onclick="selectStyle('{style_id}')">
-            <div class="style-title">{style_id.replace('_', ' ')}</div>
-            <div class="style-desc">{style_data['guidance'][:100]}...</div>
-            <div class="pod-info">
-                <div class="pod-colors">
-                    {colors_html}
-                </div>
-                <div class="pod-products">
-                    {products_html}
-                </div>
-            </div>
-        </div>
-        """
-    
-    # JavaScript for handling style selection
-    js_code = """
-    <script>
-    function selectStyle(styleId) {
-        // Remove selected class from all cards
-        document.querySelectorAll('.style-card').forEach(card => {
-            card.classList.remove('selected');
-        });
-        
-        // Add selected class to clicked card
-        document.getElementById('style-' + styleId).classList.add('selected');
-        
-        // Update the hidden style input
-        const styleInput = document.getElementById('style-input');
-        styleInput.value = styleId;
-        
-        // Trigger change event (for Gradio)
-        const event = new Event('change');
-        styleInput.dispatchEvent(event);
-    }
-    
-    // Initialize with Auto-Detect selected
-    document.addEventListener('DOMContentLoaded', () => {
-        setTimeout(() => {
-            selectStyle('Auto-Detect');
-        }, 500);
-    });
-    </script>
-    """
-    
-    return f"""
-    <div class="style-cards">
-        {style_cards_html}
-    </div>
-    {js_code}
-    """
-
-# Create the Gradio interface
 def create_demo():
     with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
-        gr.Markdown(
-            """
-            <div class="title">
-                <h1>✨ Typography Prompt Generator for POD ✨</h1>
-            </div>
-            <div class="subtitle">
-                Generate creative typography prompts for t-shirts & hoodies using Gemini 1.5 Pro
-            </div>
-            """
-        )
-        
-        with gr.Group(elem_classes="api-key-container"):
-            api_key = gr.Textbox(
-                label="Gemini API Key", 
-                placeholder="Enter your Gemini API key here...",
-                type="password",
-                info="Your API key is not stored and only used for this session"
-            )
-        
-        quote = gr.Textbox(
-            label="Your Quote/Text", 
-            placeholder="Enter the quote you want to create typography designs for...",
-            info="Example: \"hustle mode: activated\"",
-            lines=2
-        )
-        
-        with gr.Group(elem_classes="style-options"):
-            gr.Markdown("### Style Selection")
-            
-            # Visual style selection cards
-            style_cards = gr.HTML(create_style_cards())
-            
-            # Hidden field to store selected style
-            style_preference = gr.Textbox(
-                value="Auto-Detect", 
-                visible=False, 
-                elem_id="style-input"
-            )
-            
-            with gr.Row():
-                num_prompts = gr.Slider(
-                    minimum=5,
-                    maximum=15,
-                    value=10,
-                    step=1,
-                    label="Number of Prompts",
-                    info="How many unique prompts to generate"
-                )
-        
-        submit_btn = gr.Button("Generate Typography Prompts", variant="primary", size="lg")
+        gr.Markdown("""
+            <div class="title"><h1>✨ POD Marketing & Prompt Generator ✨</h1></div>
+            <div class="subtitle">Generate Amazon keywords, target audiences, niches, and design prompts for your quotes using Gemini 1.5 Pro</div>
+        """)
         
-        gr.Examples(
-            examples=example_quotes,
-            inputs=quote,
-            label="Example Quotes"
-        )
-        
-        # Output tabs for different sections
         with gr.Tabs() as tabs:
-            with gr.TabItem("Typography Prompts"):
-                output = gr.Textbox(
-                    label="Generated Typography Prompts",
-                    placeholder="Your creative prompts will appear here...",
-                    lines=15,
-                    elem_classes=["output-box"],
-                    show_copy_button=True
-                )
+            with gr.TabItem("Generate Content"):
+                with gr.Group(elem_classes="api-key-container"):
+                    api_key_input = gr.Textbox(label="Gemini API Key", placeholder="Enter your Gemini API key here...", type="password", info="Your API key is not stored and only used for this session")
+                quote_input = gr.Textbox(label="Your Quote/Text", placeholder="Enter the quote you want to analyze...", info="Example: \"good vibes only\"", lines=2)
+                
+                with gr.Group(elem_classes="style-options"):
+                    gr.Markdown("### Style Preferences for Design Prompts (Section 4)")
+                    with gr.Row():
+                        # num_prompts_slider now directly controls the number of design ideas
+                        num_prompts_slider = gr.Slider(
+                            minimum=5, maximum=15, value=7, step=1, # Default to 7
+                            label="Number of Design Ideas for Section 4", 
+                            info="Select how many design prompts to generate for Section 4."
+                        )
+                        style_preference_radio = gr.Radio(
+                            ["None (Let AI decide)", "Retro 70s", "Modern Minimal", "Hand-drawn", "Feminine/Girly", "Bold Experimental", "Silhouette", "Cartoon", "Vintage"],
+                            label="Preferred Style for Design Prompts", value="None (Let AI decide)", info="Optional: Choose a preferred style for Section 4 design prompts."
+                        )
+                
+                submit_btn = gr.Button("Generate Marketing Content & Prompts", variant="primary", size="lg")
+                gr.Examples(examples=example_quotes, inputs=quote_input, label="Example Quotes")
+                
+                prompts_output_textbox = gr.Textbox(label="🎨 Design Prompts (Section 4)", placeholder="Creative design prompts will appear here...", lines=15, elem_classes=["output-box"], show_copy_button=True)
+                marketing_insights_textbox = gr.Textbox(label="📈📅🎯 Marketing Insights (Sections 1-3)", placeholder="Amazon Keywords, Holidays/Niches, and Target Audience will appear here...", lines=15, elem_classes=["output-box", "market-box"], show_copy_button=True)
             
-            with gr.TabItem("Marketing Insights"):
-                market_output = gr.Textbox(
-                    label="POD Marketing Analysis",
-                    placeholder="Holiday/Event, Niche information, and keywords will appear here...",
-                    lines=12,
-                    elem_classes=["output-box", "market-box"],
-                    show_copy_button=True
-                )
-        
-        # Store the detected style (for reference/analytics)
-        detected_style = gr.State(value="")
-        
-        gr.Markdown(
-            """
-            <footer>
-                Typography Prompt Generator for POD | Perfect for T-shirts & Hoodies | Create unique designs that sell
-            </footer>
-            """
-        )
+            with gr.TabItem("Quote Analysis (Alternative)"):
+                with gr.Group(elem_classes="api-key-container"):
+                    analysis_api_key = gr.Textbox(label="Gemini API Key", placeholder="Enter your Gemini API key here...", type="password", info="Your API key is not stored.")
+                analysis_quote_input = gr.Textbox(label="Your Quote/Text", placeholder="Enter a quote to analyze its mood, style, colors...", lines=2)
+                analyze_btn = gr.Button("Analyze Quote", variant="primary")
+                analysis_output_textbox = gr.Textbox(label="Quote Analysis Details", placeholder="Detailed analysis of mood, style, colors, elements will appear here...", lines=10, elem_classes=["output-box"])
+            
+            with gr.TabItem("Help"):
+                gr.Markdown("""
+                    ### How to Use the POD Marketing & Prompt Generator
+                    1. **Get a Gemini API Key**: Visit [Google AI Studio](https://makersuite.google.com/) and generate an API key.
+                    2. **Enter Your API Key**: In the "Generate Content" or "Quote Analysis" tab.
+                    3. **Enter Your Quote**: The text you want to base your T-shirt design and marketing on.
+                    4. **Number of Design Ideas**: Use the slider to choose how many design prompts (5-15) you want for Section 4.
+                    5. **Choose Style (Optional)**: Select a preferred visual style for the design prompts in Section 4. If "None" is selected, the AI will choose.
+                    6. **Generate Content**: Click "Generate Marketing Content & Prompts".
+                    
+                    ### Output Sections (Generate Content Tab):
+                    *   **📈 1. AMAZON SEARCH KEYWORDS**
+                    *   **📅 2. HOLIDAYS / EVENTS / NICHES**
+                    *   **🎯 3. TARGET AUDIENCE**
+                    *   **🎨 4. PROMPTS ([Number Selected] Ideas) — STYLE: [AUTO or SELECTED STYLE]**
+                    Sections 1, 2, and 3 appear in "Marketing Insights". Section 4 appears in "Design Prompts".
+                    
+                    ### Quote Analysis Tab
+                    Offers analysis on: mood, general typography styles, color palettes, decorative elements.
+                """)
         
-        # Handle form submissions
         submit_btn.click(
-            fn=process_quote,
-            inputs=[api_key, quote, num_prompts, style_preference],
-            outputs=[output, market_output, detected_style]
+            fn=process_quote_with_style_mapping,
+            inputs=[api_key_input, quote_input, num_prompts_slider, style_preference_radio],
+            outputs=[prompts_output_textbox, marketing_insights_textbox]
         )
-    
+        analyze_btn.click(fn=analyze_quote, inputs=[analysis_api_key, analysis_quote_input], outputs=analysis_output_textbox)
+        
+        gr.HTML("""
+        <script>
+        function syncApiKeys() {
+            const apiKeyInputs = document.querySelectorAll('input[type="password"]');
+            if (apiKeyInputs.length < 2) return;
+            const mainApiKeyInput = apiKeyInputs[0];
+            const otherApiKeyInputs = Array.from(apiKeyInputs).slice(1);
+            mainApiKeyInput.addEventListener('input', function(e) {
+                otherApiKeyInputs.forEach(otherElem => {
+                    if (otherElem.value !== e.target.value) {
+                        otherElem.value = e.target.value;
+                        otherElem.dispatchEvent(new Event('input', { bubbles: true }));
+                    }
+                });
+            });
+            otherApiKeyInputs.forEach(otherInput => {
+                otherInput.addEventListener('input', function(e) {
+                    if (mainApiKeyInput.value !== e.target.value) {
+                         mainApiKeyInput.value = e.target.value;
+                         mainApiKeyInput.dispatchEvent(new Event('input', { bubbles: true }));
+                    }
+                    otherApiKeyInputs.forEach(syncTarget => {
+                        if (syncTarget !== e.target && syncTarget.value !== e.target.value) {
+                            syncTarget.value = e.target.value;
+                            syncTarget.dispatchEvent(new Event('input', { bubbles: true }));
+                        }
+                    });
+                });
+            });
+        }
+        let attempts = 0;
+        function trySync() {
+            if (document.querySelectorAll('input[type="password"]').length > 1) {
+                 syncApiKeys();
+            } else if (attempts < 10) {
+                 attempts++;
+                 setTimeout(trySync, 500);
+            }
+        }
+        if (typeof NProgress !== 'undefined') {
+            const intervalId = setInterval(() => {
+                if (!NProgress.isStarted()) {
+                    clearInterval(intervalId);
+                    trySync();
+                }
+            }, 100);
+        } else {
+            document.addEventListener('DOMContentLoaded', () => {
+                 setTimeout(trySync, 1000);
+            });
+        }
+        </script>
+        """, visible=False)
+        gr.Markdown("<footer>POD Marketing & Prompt Generator | Powered by Gemini AI</footer>")
     return demo
 
-# Launch the app
+def process_quote_with_style_mapping(api_key, quote, num_prompts, ui_style_preference_str, progress=gr.Progress()):
+    return process_quote(api_key, quote, num_prompts, ui_style_preference_str, progress)
+
 if __name__ == "__main__":
     demo = create_demo()
     demo.launch()