Added Tone and updated the banner prompt

app/ads/budget_routes.py

@@ -1,29 +0,0 @@
-# app/routes/budget_routes.py
-import logging
-from fastapi import APIRouter, HTTPException
-from typing import List
-
-from app.ads.schemas import BudgetRequest, BudgetPlan
-from app.ads.budget_service import generate_budget_plans
-
-router = APIRouter(prefix="/Ads", tags=["Ads"])
-logger = logging.getLogger(__name__)
-logger.addHandler(logging.NullHandler())
-
-
-@router.post("/price", response_model=List[BudgetPlan])
-def create_budget_options(payload: BudgetRequest):
-    """
-    Generate two budget options (daily & lifetime) for ad campaigns based on business inputs.
-    Returns a list of two objects:
-      [
-        {"type":"daily","budget":"25$/day","duration":"7 days"},
-        {"type":"lifetime","budget":"15$/day","duration":"62 days"}
-      ]
-    """
-    try:
-        plans = generate_budget_plans(payload)
-        return plans
-    except Exception as e:
-        logger.exception("Failed to generate budget plans: %s", e)
-        raise HTTPException(status_code=500, detail=str(e))

app/ads/descriptions_service.py

@@ -12,7 +12,6 @@ from app.ads.schemas import DescriptionsRequest, Persona
 logger = logging.getLogger(__name__)
 logger.addHandler(logging.NullHandler())
 
-# Ensure genai configured (harmless if already configured elsewhere)
 API_KEY = os.getenv("GEMINI_API_KEY")
 if API_KEY:
     try:
@@ -21,7 +20,6 @@ if API_KEY:
     except Exception:
         logger.exception("Failed to configure google.generativeai in descriptions service")
 
-
 def _extract_json_array(raw: str) -> str:
     start = raw.find('[')
     end = raw.rfind(']')
@@ -29,11 +27,7 @@ def _extract_json_array(raw: str) -> str:
         return raw[start:end + 1]
     return raw
 
-
 def _build_descriptions_prompt(req: DescriptionsRequest) -> str:
-    """
-    Build prompt asking Gemini to return ONLY a JSON array of strings (ad descriptions).
-    """
     try:
         personas_json = json.dumps([p.dict() for p in req.selected_personas], indent=2)
     except Exception:
@@ -41,17 +35,20 @@ def _build_descriptions_prompt(req: DescriptionsRequest) -> str:
 
     main_goal_value = req.main_goal.value
     main_goal_desc = getattr(req.main_goal, "description", "")
+    tone_enum = getattr(req, "tone", None)
+    tone_value = tone_enum.value if tone_enum is not None else "Professional"
 
     prompt = f"""
 You are an expert ad copywriter specialized in short, high-converting ad descriptions for digital ads.
 
+Tone: Use a "{tone_value}" tone for all descriptions. If tone is "Professional", write concise, formal copy. If "Casual / Friendly", be approachable and conversational. If "Bold / Persuasive", use urgency and strong value statements. If "Inspiring / Visionary", use motivational and future-focused language.
+
 Task:
 Produce exactly {req.num_descriptions} ad descriptions (each 1-2 short sentences) tailored to the business and the selected persona(s). RETURN ONLY a JSON array of strings (e.g. ["Desc 1", "Desc 2", ...]) and nothing else.
 
 Requirements:
 - Each description should be concise (max ~140 characters preferred), benefit-focused, and aligned with the business value and main goal.
-- Vary tone across descriptions (e.g., urgent, aspirational, trust-building, practical).
-- Include the main value or offer where appropriate (e.g., "MVP development", "AI integration", "fast time-to-market", "trusted SaaS partner").
+- Include the main value or offer where appropriate.
 - If the selected personas have different priorities, generate descriptions that address those priorities.
 - Goal: "{main_goal_value}" — {main_goal_desc}
 
@@ -71,10 +68,9 @@ Selected persona(s) (use these to shape descriptions):
 
 Now generate exactly {req.num_descriptions} unique ad descriptions as a JSON array of strings. No explanation, no extra text.
 """
-    logger.debug("Built descriptions prompt (len=%d) for business '%s'", len(prompt), req.business_name)
+    logger.debug("Built descriptions prompt (len=%d) for business '%s' (tone=%s)", len(prompt), req.business_name, tone_value)
     return prompt.strip()
 
-
 def generate_descriptions(req: DescriptionsRequest) -> List[str]:
     prompt = _build_descriptions_prompt(req)
 
@@ -97,7 +93,6 @@ def generate_descriptions(req: DescriptionsRequest) -> List[str]:
         logger.exception("Gemini generate_content failed for descriptions")
         raise RuntimeError(f"Gemini request failed: {e}")
 
-    # extract raw text
     raw = None
     try:
         if response and hasattr(response, "text") and response.text:

app/ads/headings_service.py

@@ -12,7 +12,6 @@ from app.ads.schemas import HeadingsRequest, Persona
 logger = logging.getLogger(__name__)
 logger.addHandler(logging.NullHandler())
 
-# Ensure Gemini SDK is configured (harmless if already configured elsewhere)
 API_KEY = os.getenv("GEMINI_API_KEY")
 if not API_KEY:
     logger.error("GEMINI_API_KEY not set; headings generation will fail if called without configuration.")
@@ -23,39 +22,32 @@ else:
     except Exception as e:
         logger.exception("Failed to configure google.generativeai in headings service: %s", e)
 
-
 def _extract_json_array(raw: str) -> str:
-    """
-    Return the first JSON array substring from raw (from '[' to ']') to be robust
-    against extra commentary in model output.
-    """
     start = raw.find('[')
     end = raw.rfind(']')
     if start != -1 and end != -1 and end > start:
         return raw[start:end + 1]
     return raw
 
-
 def _build_headings_prompt(req: HeadingsRequest) -> str:
-    """
-    Build a clear prompt asking Gemini to return ONLY a JSON array of strings (headings).
-    """
-    # Convert selected_personas to compact JSON for context
     personas_json = json.dumps([p.dict() for p in req.selected_personas], indent=2)
 
     main_goal_value = req.main_goal.value
     main_goal_desc = getattr(req.main_goal, "description", "")
+    tone_enum = getattr(req, "tone", None)
+    tone_value = tone_enum.value if tone_enum is not None else "Professional"
 
     prompt = f"""
 You are an expert copywriter specialized in short, high-converting ad headlines for digital ads.
 
+Tone: Use a "{tone_value}" tone for all headings. If tone is "Professional", keep headlines formal and trustworthy. If "Casual / Friendly", keep them approachable. If "Bold / Persuasive", use urgency and commanding language. If "Inspiring / Visionary", use aspirational language.
+
 Task:
 Produce exactly {req.num_headings} short, punchy ad headings (strings) for a paid ad campaign that target the selected personas and align with the business goal. RETURN ONLY a JSON array of strings (e.g. ["Heading 1", "Heading 2", ...]) and nothing else.
 
 Requirements:
 - Each heading should be concise (max ~60 characters), benefit-focused, and tailored to the provided personas and business goal.
 - Use active language and mention the key value when appropriate (e.g., "scale", "AI", "launch", "MVP", "secure funding", "reduce time-to-market").
-- Vary the tone across the 4 headings (e.g., urgent, aspirational, trust-building, and practical).
 - Avoid punctuation-only headlines, and do not include numbering in text.
 - If the main goal is "{main_goal_value}", use that intention as a primary framing. Goal description: {main_goal_desc}
 
@@ -75,14 +67,10 @@ Selected persona(s) (use these to shape headings):
 
 Now generate exactly {req.num_headings} unique ad headings as a JSON array of strings. No explanation, no extra text.
 """
-    logger.debug("Built headings prompt (len=%d) for business '%s'", len(prompt), req.business_name)
+    logger.debug("Built headings prompt (len=%d) for business '%s' (tone=%s)", len(prompt), req.business_name, tone_value)
     return prompt.strip()
 
-
 def generate_headings(req: HeadingsRequest) -> List[str]:
-    """
-    Call Gemini to generate ad headings and return list[str].
-    """
     prompt = _build_headings_prompt(req)
 
     model_name = "gemini-2.5-pro"
@@ -104,7 +92,6 @@ def generate_headings(req: HeadingsRequest) -> List[str]:
         logger.exception("Gemini generate_content failed for headings")
         raise RuntimeError(f"Gemini request failed: {e}")
 
-    # Extract raw text from response
     raw = None
     try:
         if response and hasattr(response, "text") and response.text:
@@ -129,7 +116,6 @@ def generate_headings(req: HeadingsRequest) -> List[str]:
         logger.error("Empty response from Gemini when generating headings")
         raise RuntimeError("Empty response from Gemini")
 
-    # Robust JSON extraction & parsing
     snippet = _extract_json_array(raw)
     try:
         parsed = json.loads(snippet)
@@ -141,7 +127,6 @@ def generate_headings(req: HeadingsRequest) -> List[str]:
         logger.error("Parsed headings JSON is not a list of strings. Parsed type: %s", type(parsed))
         raise RuntimeError("Gemini did not return a JSON array of strings as expected.")
 
-    # Normalize: ensure exactly num_headings items
     headings = parsed
     if len(headings) < req.num_headings:
         logger.warning("Gemini returned %d headings; expected %d. Returning what we have.",
@@ -150,7 +135,6 @@ def generate_headings(req: HeadingsRequest) -> List[str]:
         headings = headings[: req.num_headings]
         logger.debug("Trimmed headings to requested num_headings=%d", req.num_headings)
 
-    # Final basic cleanup (strip whitespace)
     headings = [h.strip() for h in headings]
 
     logger.info("Generated %d headings for business '%s'", len(headings), req.business_name)

app/ads/image_service.py

@@ -1,10 +1,11 @@
 import io
 import logging
-from typing import Tuple
+from typing import Tuple, List, Optional
 from google import genai
 from google.genai import types
 
-from app.ads.schemas import ImageRequest, Persona
+# Assuming this is your schema file
+from app.ads.schemas import ImageRequest, Persona, ToneEnum, GoalEnum
 
 logger = logging.getLogger(__name__)
 
@@ -15,30 +16,109 @@ def _persona_to_text(persona: Persona) -> str:
 
 def generate_image(req: ImageRequest) -> Tuple[bytes, str]:
     """
-    Generate an Ad image using Gemini 2.0 flash experimental image model.
-    Falls back to returning text prompts if Gemini returns only text.
+    Generate an Ad image using Gemini with a highly-structured prompt.
     """
+    # --- 1. Prepare dynamic prompt components ---
+
     # Safely convert Persona objects into strings
     if req.selected_personas and len(req.selected_personas) > 0:
         personas_text = "; ".join(_persona_to_text(p) for p in req.selected_personas)
     else:
         personas_text = "general target audience"
 
-    prompt = (
-        f"Create a professional advertisement image for the business '{req.business_name}'. "
-        f"Category: {req.business_category}. "
-        f"Description: {req.business_description}. "
-        f"Promotion type: {req.promotion_type}. "
-        f"Offer details: {req.offer_description}. "
-        f"Value proposition: {req.value}. "
-        f"Main goal: {req.main_goal.value}. "
-        f"Serving clients info: {req.serving_clients_info}. "
-        f"Location: {req.serving_clients_location}. "
-        f"Target persona(s): {personas_text}. "
-        "The image should be modern, vibrant, and suitable for a social media advertisement."
-    )
+    # Define aspect ratio
+    aspect_ratio_str = f"{req.width}:{req.height}"
+    if aspect_ratio_str == "1200:628":
+        aspect_ratio_desc = "1.91:1 (Landscape Link Ad)"
+    elif aspect_ratio_str == "1080:1080":
+        aspect_ratio_desc = "1:1 (Square Feed Ad)"
+    elif aspect_ratio_str == "1080:1920":
+        aspect_ratio_desc = "9:16 (Vertical Story Ad)"
+    else:
+        aspect_ratio_desc = f"{aspect_ratio_str} (Custom)"
+
+    # Create strings for optional fields
+    brand_color_str = ""
+    if req.brand_colors:
+        brand_color_str = f"* **Color Palette:** Strictly use this palette as the primary and accent colors: {', '.join(req.brand_colors)}."
+    else:
+        brand_color_str = f"* **Color Palette:** Use a professional color palette (e.g., blues, greys, with one strong accent color) that matches the '{req.tone.value}' tone."
+
+    visual_pref_text = ""
+    if req.visual_preference:
+        visual_pref_text = f"The main visual *must* be: **'{req.visual_preference}'**. This should be the element that catches the eye first."
+    else:
+        visual_pref_text = "Create a single, strong, and uncluttered focal point that represents the offer (e.g., an abstract graphic of growth, a clean icon, or a person matching the persona)."
+
+    cta_text_str = ""
+    if req.cta_text:
+        cta_text_str = f"The banner *must* also include the *exact* phrase: **'{req.cta_text}'**. This can be in a button-like shape or as clear, bold text."
+
+
+    # --- 2. The New, Detailed, and Structured Prompt ---
+
+    prompt = f'''
+**ROLE:** You are an expert Art Director at a world-class advertising agency.
+**YOUR GOAL:** To generate a *single*, professional, and high-conversion ad banner for a Meta (Facebook/Instagram) campaign.
+**YOUR TASK:** Generate one (1) image file based *only* on the strict hierarchy, components, and constraints below.
+
+---
+
+### 1. HIERARCHY OF IMPORTANCE (Strict Priority)
+
+This is the order of what the user MUST see. Your design must follow this.
+
+* **P1: FOCAL POINT (The Visual):**
+    * This is the **most important** element. It must stop the user from scrolling.
+    * It must be a single, clean, high-quality, professional image or graphic.
+    * **Visual Content:** {visual_pref_text}
+    * It must be the primary focus of the entire banner.
+
+* **P2: HEADLINE (The Offer):**
+    * This is the **second most important** element. It is the primary text.
+    * It must be bold, clear, and easy to read in 1 second.
+    * **Text Content:** It must be the *exact* phrase: **'{req.offer_description}'**
+
+* **P3: CALL-TO-ACTION (The Instruction):**
+    * This is the **third most important** element. It tells the user what to do next.
+    * It must be visually distinct (e.g., a button shape or contrasting bold text).
+    * **Text Content:** {cta_text_str}
+
+* **P4: BRANDING (The Signature):**
+    * This is the **least important** element. It is for brand recognition only.
+    * It must be small, clean, and placed in a corner (e.g., bottom-left or top-right).
+    * **Text Content:** **'{req.business_name}'** (Render this as clean text, not a complex logo).
+
+---
+
+### 2. DESIGN & LAYOUT MANDATES
+
+* **ASPECT RATIO:** **{aspect_ratio_desc} ({req.width}x{req.height}px).** This is a non-negotiable technical requirement.
+* **STYLE & TONE:** **{req.style}, {req.tone.value}.**
+* **TARGET AUDIENCE CONTEXT:** The entire design (font choice, imagery, colors) must feel professional and trustworthy to this user: **{personas_text}**.
+* **COLOR PALETTE:** {brand_color_str}
+
+---
+
+### 3. WHAT TO AVOID (CRITICAL FAILURES)
+
+* **DO NOT** add *any text* not explicitly listed in P2, P3, or P4.
+* **DO NOT** clutter the image. White space is essential. The design must be **minimalist** and **clean**.
+* **DO NOT** use generic, cheesy, or low-quality stock photos.
+* **DO NOT** use hard-to-read, cursive, or overly-stylized fonts. Readability is the top priority for text.
+* **DO NOT** make the branding (P4) large or the main focus. It must be subtle.
+
+---
+
+### FINAL OUTPUT
+
+Generate *only* the final, polished ad banner image. Do not respond with text, questions, or comments.
+'''
+
+    # --- 3. Execute the API Call ---
 
     logger.info("Requesting Gemini image generation for business '%s'", req.business_name)
+    logger.debug("Full prompt:\n%s", prompt)  # Good for debugging
 
     try:
         client = genai.Client()
@@ -61,10 +141,13 @@ def generate_image(req: ImageRequest) -> Tuple[bytes, str]:
         for part in response.candidates[0].content.parts:
             if getattr(part, "text", None):
                 logger.warning("Gemini returned text instead of image: %s", part.text[:300])
-                raise RuntimeError("Gemini returned text only, no image data found.")
+                raise RuntimeError(f"Gemini returned text only: {part.text[:300]}...")
 
         raise RuntimeError("No image data found in Gemini response.")
 
+    except AttributeError as e:
+        logger.exception(f"Failed to parse Gemini response. It's possible the API response structure is unexpected. {e}")
+        raise RuntimeError(f"Failed to parse Gemini response: {e}")
     except Exception as e:
         logger.exception("Gemini image generation failed: %s", e)
-        raise RuntimeError(f"Gemini image generation failed: {e}")
+        raise RuntimeError(f"Gemini image generation failed: {e}")

app/ads/persona_routes.py

@@ -3,12 +3,22 @@ from fastapi import APIRouter, HTTPException
 from typing import List
 from fastapi.responses import StreamingResponse
 
-from app.ads.schemas import BusinessInput, Persona, RegenerateRequest, HeadingsRequest, DescriptionsRequest, ImageRequest
+from app.ads.schemas import (
+    BusinessInput,
+    Persona,
+    RegenerateRequest,
+    HeadingsRequest,
+    DescriptionsRequest,
+    ImageRequest,
+    BudgetRequest,
+    BudgetPlan,
+)
 import io
 import app.ads.image_service as image_service
 import app.ads.headings_service as headings_service
 import app.ads.descriptions_service as descriptions_service
-from app.ads.persona_service import generate_personas , regenerate_personas
+from app.ads.persona_service import generate_personas, regenerate_personas
+from app.ads.budget_service import generate_budget_plans
 
 router = APIRouter(prefix="/Ads", tags=["Ads"])
 
@@ -29,7 +39,6 @@ def regenerate_personas_endpoint(payload: RegenerateRequest):
     try:
         personas = regenerate_personas(payload, payload.previous_personas)
     except Exception as e:
-        # return the error message to the client for quick debugging
         raise HTTPException(status_code=500, detail=str(e))
     return personas
 
@@ -41,7 +50,6 @@ def create_headings(payload: HeadingsRequest):
     try:
         return headings_service.generate_headings(payload)
     except Exception as e:
-        # Log error server-side; return HTTP 500 with message for debugging
         raise HTTPException(status_code=500, detail=str(e))
     
 @router.post("/Descriptions", response_model=List[str])
@@ -60,4 +68,15 @@ def create_image(payload: ImageRequest):
         img_bytes, mime = image_service.generate_image(payload)
         return StreamingResponse(io.BytesIO(img_bytes), media_type=mime)
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/price", response_model=List[BudgetPlan])
+def create_budget_options(payload: BudgetRequest):
+    """
+    Generate two budget options (daily & lifetime) for ad campaigns based on business inputs.
+    """
+    try:
+        plans = generate_budget_plans(payload)
+        return plans
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

app/ads/persona_service.py

@@ -9,20 +9,15 @@ from pydantic import ValidationError
 
 from app.ads.schemas import Persona, BusinessInput
 
-# module logger
 logger = logging.getLogger(__name__)
-# Avoid configuring global logging here; app-level config should set handlers/levels.
-# But ensure we have at least a NullHandler to avoid "No handler" warnings in some apps.
 logger.addHandler(logging.NullHandler())
 
-
 # initialize client (reads GEMINI_API_KEY from environment)
 API_KEY = os.getenv("GEMINI_API_KEY")
 if not API_KEY:
     logger.error("GEMINI_API_KEY environment variable not set")
     raise RuntimeError("Please set the GEMINI_API_KEY environment variable")
 
-# Configure the genai SDK (reference pattern)
 try:
     genai.configure(api_key=API_KEY)
     logger.info("Configured google.generativeai with provided API key")
@@ -30,9 +25,7 @@ except Exception as e:
     logger.exception("Failed to configure google.generativeai: %s", e)
     raise
 
-
 def _build_prompt(b: BusinessInput) -> str:
-
     examples_json = [
         {
             "name": "Startup Founders",
@@ -74,13 +67,16 @@ def _build_prompt(b: BusinessInput) -> str:
         }
     ]
 
-    # Use both the enum value and the associated description
     main_goal_value = b.main_goal.value
     main_goal_desc = getattr(b.main_goal, "description", "")
+    tone_enum = getattr(b, "tone", None)
+    tone_value = tone_enum.value if tone_enum is not None else "Professional"
 
     prompt = f'''
 You are a senior marketing strategist specialized in creating *ideal-customer / target-audience personas* for businesses. Produce exactly {b.num_personas} distinct IDEAL-CUSTOMER personas tailored to the business described below.
 
+**Tone:** Use a "{tone_value}" tone for all persona text (including descriptions and headlines). Apply the tone consistently. If tone is "Professional", use a formal, trustworthy voice. If "Casual / Friendly", be approachable and conversational. If "Bold / Persuasive", use urgency and strong calls-to-action. If "Inspiring / Visionary", use motivating, future-focused language.
+
 **Output format (required):** Return ONLY a JSON array of objects. Each object must contain these properties in this exact order:
   1. name (string)
   2. headline (string; 3-6 words)
@@ -95,11 +91,10 @@ You are a senior marketing strategist specialized in creating *ideal-customer /
 - primary buying trigger or motivation,
 - preferred channels to reach them (e.g., Instagram, LinkedIn, email, local events),
 - why they would choose this business / how the offer solves their need.
+- Use the requested tone when phrasing motivations and channels.
 
 **Do NOT include any extra top-level keys, comments, or explanation text. JSON array only.**
 
-Below are three example personas showing the exact style and level of detail I want your output to match. Use them as format examples — but produce personas specific to the business inputs that follow.
-
 EXAMPLE PERSONAS (format example):
 {json.dumps(examples_json, indent=2)}
 
@@ -117,15 +112,10 @@ Business inputs:
 Generate the {b.num_personas} personas now as a JSON array that exactly matches the schema and style shown above.
 '''
     built = prompt.strip()
-    logger.debug("Built persona prompt (goal=%s): %s", main_goal_value, built[:400] + ("…" if len(built) > 400 else ""))
+    logger.debug("Built persona prompt (goal=%s; tone=%s): %s", main_goal_value, tone_value, built[:400] + ("…" if len(built) > 400 else ""))
     return built
 
-
 def _extract_json_array(raw: str) -> str:
-    """
-    Find and return the first JSON array substring in raw text (from '[' to ']').
-    If not found, return raw as-is (parsing will attempt).
-    """
     start = raw.find('[')
     end = raw.rfind(']')
     if start != -1 and end != -1 and end > start:
@@ -135,12 +125,7 @@ def _extract_json_array(raw: str) -> str:
     logger.debug("No JSON array brackets found in raw response; returning full raw text for parsing")
     return raw
 
-
 def generate_personas(b: BusinessInput) -> List[Persona]:
-    """
-    Generate personas using Gemini. Returns a list of Persona Pydantic models.
-    Logs important steps and errors for easier debugging.
-    """
     prompt = _build_prompt(b)
 
     try:
@@ -155,11 +140,9 @@ def generate_personas(b: BusinessInput) -> List[Persona]:
         logger.info("Gemini generate_content completed in %.2fs", duration)
 
     except Exception as e:
-        # surface Gemini initialization / network errors with stack trace
         logger.exception("Gemini request failed for business '%s': %s", b.business_name, e)
         raise RuntimeError(f"Gemini request failed: {e}")
 
-    # Inspect response for text or safety block
     raw = None
     try:
         if response and hasattr(response, "text") and response.text:
@@ -184,17 +167,13 @@ def generate_personas(b: BusinessInput) -> List[Persona]:
         logger.error("Empty response received from Gemini for business '%s'", b.business_name)
         raise RuntimeError("Empty response received from Gemini")
 
-    # Extract JSON array substring (robust for extra commentary)
     json_snippet = _extract_json_array(raw)
     logger.info("Attempting to parse JSON snippet for business '%s' (snippet length=%d)", b.business_name, len(json_snippet))
 
-    # Attempt to parse JSON and validate against Persona schema
     try:
         parsed = json.loads(json_snippet)
 
-        # If model returned a dict wrapper, attempt to find the list inside
         if isinstance(parsed, dict):
-            # common wrapper keys to check
             for key in ("items", "personas", "data", "results"):
                 if key in parsed and isinstance(parsed[key], list):
                     parsed = parsed[key]
@@ -212,7 +191,6 @@ def generate_personas(b: BusinessInput) -> List[Persona]:
                 personas.append(persona)
                 logger.debug("Validated persona %d: %s", idx, persona.name)
             except ValidationError as ve:
-                # include which item failed for better debugging
                 logger.error("Persona validation failed for item %s: %s\nRaw item: %s", idx, ve, obj)
                 raise
 
@@ -220,35 +198,29 @@ def generate_personas(b: BusinessInput) -> List[Persona]:
         return personas
 
     except (json.JSONDecodeError, ValidationError, ValueError) as e:
-        # Provide helpful debug output including raw Gemini text
         logger.exception("Failed to parse/validate Gemini JSON output for business '%s'", b.business_name)
         raise RuntimeError(
             f"Failed to parse Gemini response as JSON Personas: {e}\n\nRaw Gemini response:\n{raw}"
         )
 
 def regenerate_personas(b: BusinessInput, previous_personas: List[Persona]) -> List[Persona]:
-    """
-    Generate a new set of personas given the business input AND a list of
-    previously generated personas. The model is instructed to avoid duplicating
-    the previous personas and produce distinct/improved target-audience personas.
-    """
-    # Build base prompt from existing function
     base_prompt = _build_prompt(b)
 
-    # Convert previous_personas to simple dicts (Pydantic models -> plain dicts)
     try:
         prev_list = [p.dict() if hasattr(p, "dict") else p for p in previous_personas]
     except Exception:
-        # Defensive: if previous_personas are plain dicts already
         prev_list = previous_personas
 
     prev_json = json.dumps(prev_list, indent=2)
+    tone_enum = getattr(b, "tone", None)
+    tone_value = tone_enum.value if tone_enum is not None else "Professional"
 
-    # Append clear instructions about previous personas and uniqueness requirement
     extra_instructions = f"""
 Previous personas provided (do NOT repeat these exactly):
 {prev_json}
 
+Tone: Use a "{tone_value}" tone consistently in names and descriptions. If tone is "Professional" use a formal voice; if "Casual / Friendly" be conversational; if "Bold / Persuasive" be urgency-driven; if "Inspiring / Visionary" be motivational.
+
 Instructions:
 - Produce exactly {b.num_personas} personas tailored to the same business inputs above.
 - **Do not duplicate** persona names or core audience segments included in the previous list.
@@ -261,13 +233,11 @@ Instructions:
 """
     prompt = base_prompt + "\n\n" + extra_instructions
 
-    logger.info("Regenerating personas for business '%s' with %d previous personas",
-                b.business_name, len(prev_list))
+    logger.info("Regenerating personas for business '%s' with %d previous personas (tone=%s)",
+                b.business_name, len(prev_list), tone_value)
 
-    # call model (same pattern as generate_personas)
     try:
         model_name = "gemini-2.5-pro"
-        logger.info("Initializing Gemini model for regeneration: %s", model_name)
         model = genai.GenerativeModel(model_name)
 
         start_ts = time.perf_counter()
@@ -279,7 +249,6 @@ Instructions:
         logger.exception("Gemini regenerate request failed for business '%s': %s", b.business_name, e)
         raise RuntimeError(f"Gemini regenerate request failed: {e}")
 
-    # Extract raw text (same robust logic)
     raw = None
     try:
         if response and hasattr(response, "text") and response.text:
@@ -304,7 +273,6 @@ Instructions:
         logger.error("Empty regenerate response from Gemini for business '%s'", b.business_name)
         raise RuntimeError("Empty response received from Gemini")
 
-    # Extract JSON and validate (reuse helper)
     json_snippet = _extract_json_array(raw)
     logger.info("Attempting to parse regenerated JSON snippet for business '%s' (len=%d)",
                 b.business_name, len(json_snippet))
@@ -312,7 +280,6 @@ Instructions:
     try:
         parsed = json.loads(json_snippet)
 
-        # If wrapper -> find list
         if isinstance(parsed, dict):
             for key in ("items", "personas", "data", "results"):
                 if key in parsed and isinstance(parsed[key], list):

app/ads/schemas.py

@@ -1,3 +1,4 @@
+# app/ads/schemas.py
 from enum import Enum
 from pydantic import BaseModel, Field
 from typing import List, Optional
@@ -27,6 +28,12 @@ class GoalEnum(str, Enum):
         obj.description = description
         return obj
 
+class ToneEnum(str, Enum):
+    PROFESSIONAL = "Professional"
+    CASUAL_FRIENDLY = "Casual / Friendly"
+    BOLD_PERSUASIVE = "Bold / Persuasive"
+    INSPIRING_VISIONARY = "Inspiring / Visionary"
+
 class Persona(BaseModel):
     uuid: str = Field(default_factory=lambda: str(uuid4()), description="Unique identifier for this persona")
     flag: bool = Field(False, description="Boolean flag for client use (defaults to False)")
@@ -48,7 +55,7 @@ class BusinessInput(BaseModel):
     serving_clients_info: str
     serving_clients_location: str
     num_personas: int = 3
-
+    tone: Optional[ToneEnum] = Field(ToneEnum.PROFESSIONAL, description="Tone to use when generating content. Defaults to 'Professional'.")
 
 class RegenerateRequest(BusinessInput):
     """
@@ -58,7 +65,6 @@ class RegenerateRequest(BusinessInput):
     """
     previous_personas: List[Persona]
 
-
 class HeadingsRequest(BaseModel):
     business_name: str = Field(..., example="GrowthAspired")
     business_category: str = Field(..., example="Software House")
@@ -69,11 +75,9 @@ class HeadingsRequest(BaseModel):
     main_goal: GoalEnum = Field(..., description="Primary marketing goal (enum)")
     serving_clients_info: str
     serving_clients_location: str
-    # list of previously generated or selected persona objects (use Persona model)
     selected_personas: List[Persona] = Field(..., description="List of selected persona objects to target")
-    # optional: prefer number of headings (defaults to 4)
     num_headings: Optional[int] = Field(4, description="How many headings to generate")
-
+    tone: Optional[ToneEnum] = Field(ToneEnum.PROFESSIONAL, description="Tone to use for headings. Defaults to 'Professional'.")
 
 class DescriptionsRequest(BaseModel):
     business_name: str = Field(..., example="GrowthAspired")
@@ -87,7 +91,7 @@ class DescriptionsRequest(BaseModel):
     serving_clients_location: str
     selected_personas: List[Persona] = Field(..., description="List of selected persona objects to target")
     num_descriptions: Optional[int] = Field(4, description="How many ad descriptions to generate (default 4)")
-
+    tone: Optional[ToneEnum] = Field(ToneEnum.PROFESSIONAL, description="Tone to use for descriptions. Defaults to 'Professional'.")
 
 class ImageRequest(BaseModel):
     business_name: str = Field(..., example="GrowthAspired")
@@ -100,24 +104,37 @@ class ImageRequest(BaseModel):
     serving_clients_info: str
     serving_clients_location: str
     selected_personas: List[Persona] = Field(..., description="List of selected persona objects to target")
-    # optional style/size params
     style: Optional[str] = Field("modern", description="Desired art style or mood (e.g. modern, minimal, illustrative)")
     width: Optional[int] = Field(1200, description="Image width in px")
     height: Optional[int] = Field(628, description="Image height in px")
+    tone: Optional[ToneEnum] = Field(ToneEnum.PROFESSIONAL, description="Tone for image copy or captions. Optional.")
 
-
-
+    # --- NEW FIELDS TO ADD ---
+    cta_text: Optional[str] = Field(
+        None, 
+        description="The exact call-to-action text for the banner (e.g., 'Learn More', 'Get Free Call').", 
+        example="Get Your Free Strategy Call"
+    )
+    brand_colors: Optional[List[str]] = Field(
+        None, 
+        description="A list of primary brand color hex codes.", 
+        example=["#0D47A1", "#FFC107"]
+    )
+    visual_preference: Optional[str] = Field(
+        None, 
+        description="Specific request for the main visual (e.g., 'Show a graph of growth', 'Show a professional consultant').", 
+        example="Show a graph representing website traffic growth"
+    )
+    
 class BudgetType(str, Enum):
     daily = "daily"
     lifetime = "lifetime"
 
-
 class BudgetPlan(BaseModel):
     type: BudgetType
     budget: str
     duration: str
 
-
 class BudgetRequest(BaseModel):
     business_name: str = Field(..., example="GrowthAspired")
     business_category: str = Field(..., example="Software House")
@@ -129,5 +146,5 @@ class BudgetRequest(BaseModel):
     serving_clients_info: str
     serving_clients_location: str
     selected_personas: List[Persona] = Field(..., description="List of selected persona objects to target")
-    # optional override if you want more/less options in future
     num_options: Optional[int] = Field(2, description="Number of budget option groups to return (default 2)")
+    tone: Optional[ToneEnum] = Field(ToneEnum.PROFESSIONAL, description="Tone for budget explanations (optional).")

app/main.py

@@ -22,7 +22,6 @@ from app.keywords.routes import router as keywords_router
 from app.uiux import routes as uiux_routes
 from app.mobile_usability import routes as mobile_usability
 from app.ads.persona_routes import router as persona_router
-from app.ads.budget_routes import router as budget_router
 
 # ─────────────────────────────────────────────
 # Suppress warnings
@@ -85,7 +84,6 @@ app.include_router(keywords_router)
 app.include_router(uiux_routes.router)
 app.include_router(mobile_usability.router)
 app.include_router(persona_router)
-app.include_router(budget_router)
 
 
 # CORS