"""
Prompt Engineering Training Chat – Gradio App  💖
===================================================
Dark romantic neon theme with hearts.
Deploy as a HuggingFace Space (Docker Space).
Connects to local Flask API via ngrok/tunnel.
"""

import gradio as gr
import requests
import json
import os
from datetime import datetime
from dotenv import load_dotenv,find_dotenv
from pathlib import Path
from modules.shimsalabim import ShimSalaBim
load_dotenv(find_dotenv())

# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
DEFAULT_API_URL = os.environ.get("FLASK_API_URL", "https://3d02-2a02-a459-18b8-0-23e8-aa8f-ba25-669a.ngrok-free.app")

# ---------------------------------------------------------------------------
# Prompt Engineering Tips & Lessons
# ---------------------------------------------------------------------------
PROMPT_ENG_TIPS = {
    "System Prompt": """**The System Prompt** is the most powerful tool for controlling LLM behavior.

**What it does:** Sets the "personality" and rules the model follows for the entire conversation.

**Key techniques to try:**
1. **Role Assignment** – Tell the model WHO it is: *"You are a senior sales consultant with 15 years of B2B SaaS experience."*
2. **Output Format** – Specify HOW it should respond: *"Always respond in bullet points. Start with a summary, then details."*
3. **Constraints** – Set boundaries: *"Never mention competitors by name. Keep responses under 100 words."*
4. **Tone Control** – *"Speak in a friendly, conversational tone. Use 'you' and 'we' frequently."*
5. **Step-by-step** – *"Think through the problem step by step before giving your final answer."*

**Experiment:** Try the same user prompt with different system prompts and observe how the output changes dramatically!""",

    "Temperature": """**Temperature** controls randomness/creativity in the model's output.

- **0.0 – 0.3:** Very focused, deterministic, repetitive. Great for factual Q&A, data extraction, code.
- **0.4 – 0.7:** Balanced. Good for general conversation, explanations, brainstorming.
- **0.8 – 1.5:** Creative, unpredictable. Good for storytelling, poetry, creative writing.
- **1.5+:** Chaotic, often incoherent. Useful to see what "too much creativity" looks like.

**Try this:** Set temperature to 0 and ask the same question 3 times — you'll get identical answers. Then set it to 1.2 and watch how the responses vary!""",

    "Top-P (Nucleus Sampling)": """**Top-P** (also called nucleus sampling) filters which tokens the model can choose from.

- **0.1:** Only the most likely tokens (very focused, similar to low temperature).
- **0.5:** Moderate filtering.
- **0.9 – 1.0:** Almost all tokens are candidates (more diverse).

**The difference from temperature:** Temperature reshapes the probability distribution; Top-P cuts off the tail. They work together!

**Try this:** Set temperature=0.8, top_p=0.1 → focused but not robotic. Then top_p=0.95 → much more varied.""",

    "Top-K": """**Top-K** limits the model to only choosing from the K most likely next tokens.

- **K=1:** Always picks the single most likely token (greedy decoding).
- **K=10-40:** Reasonable range for most tasks.
- **K=100+:** Very permissive, can produce unexpected results.

**When to use:** Top-K is a "hard cutoff" — it completely eliminates unlikely tokens. Top-P is "softer" — it adapts based on probability distribution.

**Try this:** Set top_k=1 and temperature=1.0. You'll see temperature has no effect because only 1 token is available!""",

    "Max Tokens": """**Max Tokens** controls the maximum length of the model's response.

- **50-100:** Short answers, ideal for classification, yes/no, single facts.
- **200-500:** Medium responses, good for explanations, email drafts.
- **500-2000:** Long-form content, articles, detailed analysis.

**Tip for prompt engineering:** If you want concise answers, set max_tokens LOW (100-150) AND say in the system prompt "Keep responses brief." The combination is more reliable than either alone.

**Try this:** Ask "Explain quantum computing" with max_tokens=50, then max_tokens=500. See how the model adapts!""",

    "Repeat Penalty": """**Repeat Penalty** discourages the model from repeating the same text.

- **1.0:** No penalty (model may repeat itself).
- **1.1-1.2:** Mild penalty (default, good for most uses).
- **1.3-1.5:** Strong penalty (may produce awkward phrasing to avoid repetition).
- **2.0+:** Extreme — the model will contort its output to never repeat.

**When to increase:** If the model gets stuck in loops ("The cat sat. The cat sat. The cat sat..."), increase this value.

**Try this:** Set repeat_penalty=1.0 and ask for a long response — watch for repetition. Then set it to 1.5 and compare.""",

    "Frequency & Presence Penalty": """**Frequency Penalty** reduces the likelihood of tokens that have already appeared frequently. It penalizes based on HOW OFTEN a token appeared.

**Presence Penalty** reduces the likelihood of ANY token that has appeared at least once. It encourages the model to talk about NEW topics.

- **Frequency 0.0 – 0.5:** Subtle reduction of repetition.
- **Presence 0.0 – 0.5:** Encourages topic diversity.

**Practical use:** For a sales email, you might want presence_penalty=0.3 to keep the model from fixating on one selling point.

**Try this:** Ask "List 10 benefits of our product" with presence_penalty=0 vs presence_penalty=0.8. The higher value will push the model toward more diverse points.""",

    "n_ctx (Context Window)": """**n_ctx** is the context window size — how many tokens the model can "see" at once (including both your prompt and its response).

- **512:** Very limited. Only short conversations.
- **2048:** Good for most single-turn Q&A and moderate conversations.
- **4096+:** Needed for long documents or extended chat history.

**Important:** n_ctx is set when the server starts (it determines how much RAM/VRAM to allocate). Changing it in the UI sends a request, but the server must be restarted for it to take effect.

**Tradeoff:** Larger n_ctx = more memory usage but can handle longer conversations. Your model has a maximum n_ctx it was trained on (e.g., 4096 for Llama 2, 8192+ for some newer models).""",

    "Stop Sequences": """**Stop Sequences** tell the model to stop generating when it encounters specific text.

Common uses:
- `"\\nUser:"` – Stop before the model starts simulating user messages.
- `"\\n\\n"` – Stop at double newlines (keeps responses to one paragraph).
- `"<|end|>"` – Model-specific end tokens.
- `"---"` – Stop before generating separators.

**Try this:** Set a stop sequence of `"."` (a period) — the model will stop after the first sentence! Remove it and it generates a full paragraph.""",

    "Effective Prompting Patterns": """**Proven patterns for better LLM outputs:**

1. **Few-Shot Examples:**
   *"Here are examples of good sales emails:*
   *Example 1: [your example]*
   *Example 2: [your example]*
   *Now write a similar email for [new situation]."*

2. **Chain of Thought:**
   *"Think step by step: First analyze the customer's needs, then identify our best matching product, then craft the pitch."*

3. **Specify the Audience:**
   *"Write this for a CTO who is technical but also cares about ROI. She has 15 minutes for this meeting."*

4. **Provide Structure:**
   *"Format your response as: 1) Key Insight, 2) Supporting Evidence, 3) Recommended Action."*

5. **Negative Instructions:**
   *"Do NOT use jargon. Do NOT mention pricing. Do NOT write more than 3 paragraphs."*

6. **Iterative Refinement:**
   Start simple, read the output, then refine your prompt based on what you liked and didn't like.""",
}


# ---------------------------------------------------------------------------
# API Communication
# ---------------------------------------------------------------------------

def check_connection(api_url: str) -> str:
    """Test the connection to the Flask API."""
    try:
        resp = requests.get(f"{api_url.rstrip('/')}/health", timeout=10)
        if resp.status_code == 200:
            data = resp.json()
            return f"💖 Connected! Model: {data.get('model', 'unknown')}, n_ctx: {data.get('n_ctx', '?')}"
        return f"⚠️ Server responded with status {resp.status_code}: {resp.text}"
    except requests.exceptions.ConnectionError:
        return "💔 Cannot connect. Is the server running? Is the tunnel active?"
    except requests.exceptions.Timeout:
        return "💔 Connection timed out. The server might be slow or unreachable."
    except Exception as e:
        return f"💔 Error: {str(e)}"


def send_chat(
    message: str,
    chat_history: list,
    api_url: str,
    system_prompt: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
    top_k: int,
    repeat_penalty: float,
    frequency_penalty: float,
    presence_penalty: float,
    n_ctx: int,
    stop_sequences: str,
) -> tuple:
    """Send a chat message to the Flask API and get a response."""

    if not message.strip():
        return "", chat_history, ""

    # Build messages array in OpenAI format
    messages = []
    if system_prompt.strip():
        messages.append({"role": "system", "content": system_prompt.strip()})

    # Add conversation history
    for user_msg, assistant_msg in chat_history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})

    # Add current message
    messages.append({"role": "user", "content": message.strip()})

    # Parse stop sequences
    stop = []
    if stop_sequences.strip():
        stop = [s.strip() for s in stop_sequences.split(",") if s.strip()]

    payload = {
        "messages": messages,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "top_k": top_k,
        "repeat_penalty": repeat_penalty,
        "frequency_penalty": frequency_penalty,
        "presence_penalty": presence_penalty,
        "n_ctx": n_ctx,
        "stop": stop if stop else None,
    }

    debug_info = f"**Request Payload:**\n```json\n{json.dumps(payload, indent=2)}\n```"

    try:
        resp = requests.post(
            f"{api_url.rstrip('/')}/chat",
            json=payload,
            timeout=120,
        )

        if resp.status_code == 200:
            data = resp.json()
            assistant_content = data.get("message", {}).get("content", "")
            usage = data.get("usage", {})
            elapsed = data.get("elapsed_seconds", "?")

            stats = (
                f"💖 **Tokens:** Prompt {usage.get('prompt_tokens', '?')} → "
                f"Completion {usage.get('completion_tokens', '?')} → "
                f"Total {usage.get('total_tokens', '?')}\n"
                f"⏱️ **Time:** {elapsed}s\n"
            )
            full_debug = f"{stats}\n{debug_info}"

            chat_history.append((message, assistant_content))
            return "", chat_history, full_debug
        else:
            error_msg = f"💔 API Error ({resp.status_code}): {resp.text}"
            chat_history.append((message, error_msg))
            return "", chat_history, debug_info

    except requests.exceptions.ConnectionError:
        error_msg = "💔 Cannot connect to the API. Check if the server is running and the tunnel is active."
        chat_history.append((message, error_msg))
        return "", chat_history, debug_info
    except requests.exceptions.Timeout:
        error_msg = "💔 Request timed out (120s). The model might be taking too long."
        chat_history.append((message, error_msg))
        return "", chat_history, debug_info
    except Exception as e:
        error_msg = f"💔 Error: {str(e)}"
        chat_history.append((message, error_msg))
        return "", chat_history, debug_info


def send_completion(
    prompt: str,
    api_url: str,
    system_prompt: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
    top_k: int,
    repeat_penalty: float,
    frequency_penalty: float,
    presence_penalty: float,
    n_ctx: int,
    stop_sequences: str,
) -> tuple:
    """Send a raw completion request (no chat history) and get the result."""
    if not prompt.strip():
        return "", ""

    stop = []
    if stop_sequences.strip():
        stop = [s.strip() for s in stop_sequences.split(",") if s.strip()]

    payload = {
        "prompt": prompt.strip(),
        "system_prompt": system_prompt.strip(),
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "top_k": top_k,
        "repeat_penalty": repeat_penalty,
        "frequency_penalty": frequency_penalty,
        "presence_penalty": presence_penalty,
        "n_ctx": n_ctx,
        "stop": stop if stop else None,
    }

    debug_info = f"**Request Payload:**\n```json\n{json.dumps(payload, indent=2)}\n```"

    try:
        resp = requests.post(
            f"{api_url.rstrip('/')}/generate",
            json=payload,
            timeout=120,
        )

        if resp.status_code == 200:
            data = resp.json()
            text = data.get("text", "")
            usage = data.get("usage", {})
            elapsed = data.get("elapsed_seconds", "?")

            stats = (
                f"💖 **Tokens:** Prompt {usage.get('prompt_tokens', '?')} → "
                f"Completion {usage.get('completion_tokens', '?')} → "
                f"Total {usage.get('total_tokens', '?')}\n"
                f"⏱️ **Time:** {elapsed}s\n"
            )
            return text, f"{stats}\n{debug_info}"
        else:
            return f"💔 API Error ({resp.status_code}): {resp.text}", debug_info

    except Exception as e:
        return f"💔 Error: {str(e)}", debug_info


# ---------------------------------------------------------------------------
# Presets for quick experimentation
# ---------------------------------------------------------------------------

PRESETS = {
    "💖 Default": {
        "system_prompt": "You are a helpful, harmless, and honest assistant.",
        "max_tokens": 512,
        "temperature": 0.7,
        "top_p": 0.9,
        "top_k": 40,
        "repeat_penalty": 1.1,
        "frequency_penalty": 0.0,
        "presence_penalty": 0.0,
        "stop": "",
    },
    "📊 Factual / Analytical": {
        "system_prompt": "You are a precise, factual assistant. Always provide accurate information. If you're unsure, say so. Use structured formats like numbered lists and tables when appropriate.",
        "max_tokens": 300,
        "temperature": 0.2,
        "top_p": 0.8,
        "top_k": 20,
        "repeat_penalty": 1.15,
        "frequency_penalty": 0.0,
        "presence_penalty": 0.0,
        "stop": "",
    },
    "✍️ Creative Writer": {
        "system_prompt": "You are a creative and imaginative writer. Be vivid, expressive, and original. Use metaphors, sensory details, and varied sentence structures. Take creative risks.",
        "max_tokens": 800,
        "temperature": 1.0,
        "top_p": 0.95,
        "top_k": 80,
        "repeat_penalty": 1.05,
        "frequency_penalty": 0.2,
        "presence_penalty": 0.2,
        "stop": "",
    },
    "💕 Sales Coach": {
        "system_prompt": "You are a senior sales coach who helps sales representatives improve their pitch, objection handling, and closing techniques. Give specific, actionable advice with examples. Be encouraging but honest about areas for improvement.",
        "max_tokens": 500,
        "temperature": 0.6,
        "top_p": 0.9,
        "top_k": 40,
        "repeat_penalty": 1.1,
        "frequency_penalty": 0.1,
        "presence_penalty": 0.1,
        "stop": "",
    },
    "🧠 Step-by-Step Reasoner": {
        "system_prompt": "You are a logical, step-by-step reasoning assistant. Always break down problems into clear steps. Show your reasoning process. Use 'Step 1:', 'Step 2:', etc. format. Double-check your logic before giving the final answer.",
        "max_tokens": 600,
        "temperature": 0.3,
        "top_p": 0.85,
        "top_k": 30,
        "repeat_penalty": 1.1,
        "frequency_penalty": 0.0,
        "presence_penalty": 0.0,
        "stop": "",
    },
    "📝 Concise Responder": {
        "system_prompt": "You are a concise assistant. Keep ALL responses under 50 words. Be direct and to the point. Never add filler or unnecessary elaboration.",
        "max_tokens": 80,
        "temperature": 0.5,
        "top_p": 0.9,
        "top_k": 40,
        "repeat_penalty": 1.15,
        "frequency_penalty": 0.0,
        "presence_penalty": 0.0,
        "stop": "",
    },
}


# ---------------------------------------------------------------------------
# Custom Dark Romantic Neon CSS
# ---------------------------------------------------------------------------

DARK_ROMANTIC_CSS = """
/* ═══════════════════════════════════════════════════
   DARK ROMANTIC NEON THEME
   Black bg + Neon Pink/Magenta/Purple accents + Hearts
   ═══════════════════════════════════════════════════ */

@import url('https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600;700&display=swap');

/* ── Root overrides ── */
:root {
  --neon-pink: #ff2d7b;
  --neon-magenta: #ff00ff;
  --neon-rose: #ff6b9d;
  --neon-purple: #b44dff;
  --neon-lavender: #d68fff;
  --dark-bg: #0a0a0a;
  --dark-surface: #111111;
  --dark-card: #161616;
  --dark-input: #1a1a1a;
  --dark-border: #2a1a24;
  --text-primary: #f0e6ef;
  --text-secondary: #b8a0b5;
  --text-dim: #7a6578;
  --glow-pink: 0 0 10px rgba(255,45,123,0.3), 0 0 20px rgba(255,45,123,0.15);
  --glow-purple: 0 0 10px rgba(180,77,255,0.3), 0 0 20px rgba(180,77,255,0.15);
}

/* ── Body & overall background ── */
body, .gradio-container {
  background: var(--dark-bg) !important;
  color: var(--text-primary) !important;
  font-family: 'Quicksand', sans-serif !important;
}

.main {
  background: var(--dark-bg) !important;
}

/* ── Animated heart background ── */
.gradio-container::before {
  content: '';
  position: fixed;
  top: 0; left: 0; right: 0; bottom: 0;
  background:
    radial-gradient(circle at 15% 25%, rgba(255,45,123,0.06) 0%, transparent 50%),
    radial-gradient(circle at 85% 75%, rgba(180,77,255,0.06) 0%, transparent 50%),
    radial-gradient(circle at 50% 50%, rgba(255,0,255,0.03) 0%, transparent 70%);
  pointer-events: none;
  z-index: 0;
}

/* ── Hearts floating animation ── */
@keyframes floatHeart {
  0%   { transform: translateY(100vh) rotate(0deg); opacity: 0; }
  10%  { opacity: 0.15; }
  90%  { opacity: 0.15; }
  100% { transform: translateY(-10vh) rotate(360deg); opacity: 0; }
}

.gradio-container::after {
  content: '♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥';
  position: fixed;
  top: 0; left: 0; right: 0;
  font-size: 24px;
  color: var(--neon-pink);
  letter-spacing: 80px;
  word-spacing: 120px;
  animation: floatHeart 25s linear infinite;
  pointer-events: none;
  z-index: 0;
  opacity: 0.08;
}

/* ── All panels, cards, containers ── */
.panel, .card, .block, .gr-panel, .gr-card,
.contain .card, .contain .block,
[data-testid="block"], .gr-box {
  background: var(--dark-card) !important;
  border-color: var(--dark-border) !important;
  border-radius: 12px !important;
}

/* ── Section backgrounds ── */
section, .gr-group, .gr-form {
  background: var(--dark-surface) !important;
  border-color: var(--dark-border) !important;
}

/* ── Input & textarea styling ── */
input[type="text"], textarea, select,
.gr-input, .gr-text-input, .gr-textarea,
[data-testid="textbox"] input, [data-testid="textbox"] textarea {
  background: var(--dark-input) !important;
  color: var(--text-primary) !important;
  border: 1px solid var(--dark-border) !important;
  border-radius: 8px !important;
  caret-color: var(--neon-pink) !important;
}

input[type="text"]:focus, textarea:focus, select:focus,
.gr-input:focus, .gr-text-input:focus, .gr-textarea:focus {
  border-color: var(--neon-pink) !important;
  box-shadow: var(--glow-pink) !important;
  outline: none !important;
}

/* ── Slider styling ── */
input[type="range"], .gr-slider input[type="range"] {
  accent-color: var(--neon-pink) !important;
}

.gr-slider .range-wrap .range-data .range-info {
  color: var(--neon-rose) !important;
}

/* ── Dropdown / Select ── */
.gr-dropdown, select, .gr-select {
  background: var(--dark-input) !important;
  color: var(--text-primary) !important;
  border: 1px solid var(--dark-border) !important;
}

/* ── Buttons ── */
button.primary, .gr-button-primary, .btn-primary {
  background: linear-gradient(135deg, var(--neon-pink), var(--neon-purple)) !important;
  color: #ffffff !important;
  border: none !important;
  border-radius: 10px !important;
  font-weight: 600 !important;
  letter-spacing: 0.5px !important;
  box-shadow: var(--glow-pink) !important;
  transition: all 0.3s ease !important;
}

button.primary:hover, .gr-button-primary:hover, .btn-primary:hover {
  background: linear-gradient(135deg, var(--neon-rose), var(--neon-magenta)) !important;
  box-shadow: 0 0 15px rgba(255,45,123,0.5), 0 0 30px rgba(255,0,255,0.3) !important;
  transform: translateY(-1px) !important;
}

button.secondary, .gr-button-secondary, .btn-secondary {
  background: var(--dark-card) !important;
  color: var(--neon-rose) !important;
  border: 1px solid var(--neon-pink) !important;
  border-radius: 10px !important;
  transition: all 0.3s ease !important;
}

button.secondary:hover, .gr-button-secondary:hover, .btn-secondary:hover {
  background: rgba(255,45,123,0.1) !important;
  box-shadow: var(--glow-pink) !important;
}

button.stop, .gr-button-stop {
  background: var(--dark-card) !important;
  color: #ff4466 !important;
  border: 1px solid #ff4466 !important;
  border-radius: 10px !important;
}

button.stop:hover, .gr-button-stop:hover {
  background: rgba(255,68,102,0.1) !important;
  box-shadow: 0 0 10px rgba(255,68,102,0.3) !important;
}

/* ── Chatbot area ── */
.gr-chatbot, [data-testid="chatbot"] {
  background: var(--dark-input) !important;
  border: 1px solid var(--dark-border) !important;
  border-radius: 12px !important;
}

.gr-chatbot .message.user, [data-testid="chatbot"] .message.user {
  background: linear-gradient(135deg, rgba(255,45,123,0.15), rgba(180,77,255,0.15)) !important;
  color: var(--text-primary) !important;
  border-left: 3px solid var(--neon-pink) !important;
  border-radius: 0 10px 10px 0 !important;
}

.gr-chatbot .message.bot, [data-testid="chatbot"] .message.bot,
.gr-chatbot .message.assistant, [data-testid="chatbot"] .message.assistant {
  background: rgba(180,77,255,0.08) !important;
  color: var(--text-primary) !important;
  border-left: 3px solid var(--neon-purple) !important;
  border-radius: 0 10px 10px 0 !important;
}

/* ── Labels ── */
label, .gr-label, [data-testid="label"] {
  color: var(--neon-rose) !important;
  font-weight: 600 !important;
}

/* ── Info text under controls ── */
.gr-info, .gr-input-info, [data-testid="info"] {
  color: var(--text-dim) !important;
  font-size: 0.85em !important;
}

/* ── Markdown text ── */
.markdown-text, .gr-markdown, .prose {
  color: var(--text-primary) !important;
}

.markdown-text h1, .gr-markdown h1, .prose h1,
.markdown-text h2, .gr-markdown h2, .prose h2,
.markdown-text h3, .gr-markdown h3, .prose h3 {
  color: var(--neon-rose) !important;
  border-color: var(--dark-border) !important;
}

.markdown-text strong, .gr-markdown strong, .prose strong {
  color: var(--neon-lavender) !important;
}

.markdown-text code, .gr-markdown code, .prose code {
  background: var(--dark-input) !important;
  color: var(--neon-pink) !important;
  border: 1px solid var(--dark-border) !important;
  border-radius: 4px !important;
}

.markdown-text a, .gr-markdown a, .prose a {
  color: var(--neon-pink) !important;
}

.markdown-text pre, .gr-markdown pre, .prose pre {
  background: var(--dark-input) !important;
  border: 1px solid var(--dark-border) !important;
  border-radius: 8px !important;
}

.markdown-text pre code, .gr-markdown pre code, .prose pre code {
  border: none !important;
}

/* ── Horizontal rules ── */
hr, .gr-hr {
  border-color: var(--dark-border) !important;
  background: linear-gradient(90deg, transparent, var(--neon-pink), transparent) !important;
  height: 1px !important;
}

/* ── Scrollbar ── */
::-webkit-scrollbar {
  width: 8px;
  height: 8px;
}
::-webkit-scrollbar-track {
  background: var(--dark-bg) !important;
}
::-webkit-scrollbar-thumb {
  background: var(--neon-pink) !important;
  border-radius: 4px;
  opacity: 0.5;
}
::-webkit-scrollbar-thumb:hover {
  background: var(--neon-rose) !important;
}

/* ── Tab styling ── */
.tab-nav, .gr-tab-nav {
  border-color: var(--dark-border) !important;
}

.tab-nav button, .gr-tab-nav button {
  color: var(--text-secondary) !important;
}

.tab-nav button.selected, .gr-tab-nav button.selected {
  color: var(--neon-pink) !important;
  border-color: var(--neon-pink) !important;
}

/* ── Title glow effect ── */
.title-glow {
  text-shadow: 0 0 10px rgba(255,45,123,0.5), 0 0 20px rgba(255,0,255,0.3);
}

/* ── Heart dividers ── */
.heart-divider {
  text-align: center;
  color: var(--neon-pink);
  font-size: 18px;
  letter-spacing: 12px;
  opacity: 0.4;
  margin: 8px 0;
  text-shadow: 0 0 8px rgba(255,45,123,0.4);
}

/* ── Neon border accent ── */
.neon-border {
  border: 1px solid var(--neon-pink) !important;
  box-shadow: var(--glow-pink) !important;
  border-radius: 12px !important;
  padding: 16px !important;
  background: var(--dark-card) !important;
}

/* ── Connection status glow ── */
#connection-status input, #connection-status textarea {
  font-family: 'Quicksand', sans-serif !important;
}

/* ── API URL monospace ── */
#api-url input {
  font-family: 'Courier New', monospace !important;
  color: var(--neon-lavender) !important;
}

/* ── Placeholder text ── */
::placeholder {
  color: var(--text-dim) !important;
}

/* ── Row dividers with hearts ── */
.heart-separator {
  display: flex;
  align-items: center;
  gap: 12px;
  margin: 16px 0;
}
.heart-separator::before,
.heart-separator::after {
  content: '';
  flex: 1;
  height: 1px;
  background: linear-gradient(90deg, transparent, var(--neon-pink), transparent);
}

/* ── Tooltip / popup ── */
.gr-tooltip, .tooltip {
  background: var(--dark-card) !important;
  color: var(--text-primary) !important;
  border: 1px solid var(--neon-pink) !important;
}

/* ── Footer ── */
footer {
  display: none !important;
}

/* ── Gradio built-in theme overrides ── */
.gap { gap: 8px !important; }

/* ── Accordion ── */
.gr-accordion, details {
  background: var(--dark-card) !important;
  border-color: var(--dark-border) !important;
}

.gr-accordion summary, details summary {
  color: var(--neon-rose) !important;
}

/* ── Bubble chat layout colors ── */
.message.bubble.user {
  background: linear-gradient(135deg, rgba(255,45,123,0.2), rgba(180,77,255,0.15)) !important;
}
.message.bubble.bot, .message.bubble.assistant {
  background: rgba(214,143,255,0.1) !important;
}
"""


# ---------------------------------------------------------------------------
# Build the Gradio Interface
# ---------------------------------------------------------------------------

def apply_preset(preset_name: str):
    """Apply a preset and return all parameter values."""
    if preset_name in PRESETS:
        p = PRESETS[preset_name]
        return (
            p["system_prompt"],
            p["max_tokens"],
            p["temperature"],
            p["top_p"],
            p["top_k"],
            p["repeat_penalty"],
            p["frequency_penalty"],
            p["presence_penalty"],
            p["stop"],
        )
    return [gr.update()] * 9


def build_ui():
    """Build the full Gradio interface with dark romantic neon theme."""

    with gr.Blocks(
        title="💖 Prompt Engineering Lab",
        theme=gr.themes.Base(
            primary_hue="pink",
            secondary_hue="purple",
            neutral_hue="stone",
        ),
        css=DARK_ROMANTIC_CSS,
    ) as demo:

        # ---- Header ----
        gr.HTML("""
        <div style="text-align: center; padding: 20px 0 10px 0;">
            <h1 style="
                font-family: 'Quicksand', sans-serif;
                font-size: 2.8em;
                font-weight: 700;
                margin: 0;
                background: linear-gradient(135deg, #ff2d7b, #ff00ff, #b44dff);
                -webkit-background-clip: text;
                -webkit-text-fill-color: transparent;
                text-shadow: none;
                filter: drop-shadow(0 0 20px rgba(255,45,123,0.4));
            ">💖 Prompt Engineering Lab 💖</h1>
            <p style="
                font-family: 'Quicksand', sans-serif;
                font-size: 1.2em;
                color: #b8a0b5;
                margin: 8px 0 0 0;
            ">Learn prompt engineering by experimenting with every LLM parameter ♥</p>
        </div>
        """)

        # ---- Heart Divider ----
        gr.HTML('<div class="heart-divider">♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥</div>')

        # ---- API Connection ----
        with gr.Row():
            api_url = gr.Textbox(
                value=DEFAULT_API_URL,
                label="🔗 Flask API URL (your ngrok/tunnel URL)",
                placeholder="https://your-ngrok-url.ngrok-free.app",
                elem_id="api-url",
                scale=4,
            )
            connect_btn = gr.Button("💖 Test Connection", variant="primary", scale=1)
            connection_status = gr.Textbox(
                label="Status",
                interactive=False,
                scale=2,
                elem_id="connection-status",
            )

        connect_btn.click(
            fn=check_connection,
            inputs=[api_url],
            outputs=[connection_status],
        )

        # ---- Heart Divider ----
        gr.HTML('<div class="heart-divider">♥ ♥ ♥ ♥ ♥ ♥</div>')

        # ---- Main Layout: Chat + Settings ----
        with gr.Row():

            # ────────────────────────────────────────────
            # Left: Chat Interface
            # ────────────────────────────────────────────
            with gr.Column(scale=3):

                gr.HTML("""
                <div style="
                    background: linear-gradient(135deg, rgba(255,45,123,0.1), rgba(180,77,255,0.1));
                    border: 1px solid #2a1a24;
                    border-radius: 12px;
                    padding: 12px 16px;
                    margin-bottom: 8px;
                ">
                    <h2 style="margin:0; color:#ff6b9d; font-family:'Quicksand',sans-serif;">
                        💬 Chat Mode
                    </h2>
                    <p style="margin:4px 0 0 0; color:#7a6578; font-size:0.9em;">
                        Multi-turn conversation with memory ♥
                    </p>
                </div>
                """)

                chatbot = gr.Chatbot(
                    label="Conversation",
                    height=450,
                    layout="bubble",
                )

                with gr.Row():
                    chat_input = gr.Textbox(
                        label="Your message",
                        placeholder="Type your message here... 💕",
                        scale=5,
                        lines=2,
                    )
                    chat_send_btn = gr.Button("Send 💖", variant="primary", scale=1)

                chat_debug = gr.Markdown(
                    value="*Debug info will appear here after each message...*",
                    label="Debug Info",
                )

                clear_chat_btn = gr.Button("💔 Clear Chat History", variant="stop")

                # Heart Divider
                gr.HTML('<div class="heart-divider">♥ ♥ ♥</div>')

                gr.HTML("""
                <div style="
                    background: linear-gradient(135deg, rgba(180,77,255,0.1), rgba(255,0,255,0.1));
                    border: 1px solid #2a1a24;
                    border-radius: 12px;
                    padding: 12px 16px;
                    margin-bottom: 8px;
                ">
                    <h2 style="margin:0; color:#d68fff; font-family:'Quicksand',sans-serif;">
                        📝 Completion Mode
                    </h2>
                    <p style="margin:4px 0 0 0; color:#7a6578; font-size:0.9em;">
                        Single-turn, no history — test prompts in isolation ♥
                    </p>
                </div>
                """)

                with gr.Row():
                    completion_prompt = gr.Textbox(
                        label="Prompt",
                        placeholder="Enter your prompt here... 💕",
                        lines=4,
                        scale=5,
                    )
                    completion_btn = gr.Button("Generate 💖", variant="primary", scale=1)

                completion_output = gr.Textbox(
                    label="Model Output",
                    lines=6,
                )
                completion_debug = gr.Markdown(value="*Debug info will appear here...*")

            # ────────────────────────────────────────────
            # Right: Settings Panel
            # ────────────────────────────────────────────
            with gr.Column(scale=2):

                # ---- Presets ----
                gr.HTML("""
                <div class="neon-border" style="margin-bottom: 12px;">
                    <h3 style="margin:0 0 6px 0; color:#ff6b9d; font-family:'Quicksand',sans-serif;">
                        ⚡ Quick Presets
                    </h3>
                    <p style="margin:0; color:#7a6578; font-size:0.85em;">
                        Load a preset to see how different settings create different behaviors 💖
                    </p>
                </div>
                """)
                preset_dropdown = gr.Dropdown(
                    choices=list(PRESETS.keys()),
                    value=list(PRESETS.keys())[0],
                    label="Choose a preset",
                )
                apply_preset_btn = gr.Button("Apply Preset 💖", variant="secondary")

                # Heart Divider
                gr.HTML('<div class="heart-divider">♥ ♥</div>')

                # ---- System Prompt ----
                gr.HTML("""
                <div style="
                    background: linear-gradient(135deg, rgba(255,45,123,0.08), rgba(255,0,255,0.05));
                    border-left: 3px solid #ff2d7b;
                    border-radius: 0 8px 8px 0;
                    padding: 8px 12px;
                    margin-bottom: 8px;
                ">
                    <h3 style="margin:0; color:#ff2d7b; font-family:'Quicksand',sans-serif;">
                        🎭 System Prompt
                    </h3>
                </div>
                """)
                system_prompt = gr.Textbox(
                    value=PRESETS["💖 Default"]["system_prompt"],
                    label="System Prompt",
                    placeholder="Define the AI's role, personality, and rules... 💕",
                    lines=5,
                    info="This sets the AI's behavior for the entire conversation.",
                )

                # ---- Generation Parameters ----
                gr.HTML("""
                <div style="
                    background: linear-gradient(135deg, rgba(180,77,255,0.08), rgba(214,143,255,0.05));
                    border-left: 3px solid #b44dff;
                    border-radius: 0 8px 8px 0;
                    padding: 8px 12px;
                    margin-bottom: 8px;
                ">
                    <h3 style="margin:0; color:#b44dff; font-family:'Quicksand',sans-serif;">
                        ⚙️ Generation Parameters
                    </h3>
                </div>
                """)

                max_tokens = gr.Slider(
                    minimum=16, maximum=4096, value=512, step=16,
                    label="Max Tokens",
                    info="Maximum response length. Higher = longer 💖",
                )
                temperature = gr.Slider(
                    minimum=0.0, maximum=2.0, value=0.7, step=0.05,
                    label="🌡️ Temperature",
                    info="0 = deterministic, 1 = balanced, 2 = creative/chaotic 💕",
                )
                top_p = gr.Slider(
                    minimum=0.0, maximum=1.0, value=0.9, step=0.05,
                    label="Top-P (Nucleus Sampling)",
                    info="0.1 = focused, 1.0 = all tokens 💖",
                )
                top_k = gr.Slider(
                    minimum=1, maximum=200, value=40, step=1,
                    label="Top-K",
                    info="Choose from K most likely next tokens 💕",
                )
                repeat_penalty = gr.Slider(
                    minimum=1.0, maximum=2.0, value=1.1, step=0.05,
                    label="🔁 Repeat Penalty",
                    info="1.0 = no penalty, 1.5+ = strong anti-repetition 💖",
                )
                frequency_penalty = gr.Slider(
                    minimum=0.0, maximum=2.0, value=0.0, step=0.05,
                    label="Frequency Penalty",
                    info="Penalizes based on how often tokens appeared 💕",
                )
                presence_penalty = gr.Slider(
                    minimum=0.0, maximum=2.0, value=0.0, step=0.05,
                    label="Presence Penalty",
                    info="Encourages new topics 💖",
                )

                # ---- Context & Stop ----
                gr.HTML('<div class="heart-divider">♥ ♥</div>')

                gr.HTML("""
                <div style="
                    background: linear-gradient(135deg, rgba(255,0,255,0.08), rgba(255,45,123,0.05));
                    border-left: 3px solid #ff00ff;
                    border-radius: 0 8px 8px 0;
                    padding: 8px 12px;
                    margin-bottom: 8px;
                ">
                    <h3 style="margin:0; color:#ff00ff; font-family:'Quicksand',sans-serif;">
                        📐 Context & Stopping
                    </h3>
                </div>
                """)
                n_ctx = gr.Slider(
                    minimum=256, maximum=8192, value=2048, step=256,
                    label="n_ctx (Context Window)",
                    info="⚠️ Requires server restart. Total token budget (prompt + response) 💖",
                )
                stop_sequences = gr.Textbox(
                    value="",
                    label="Stop Sequences (comma-separated)",
                    placeholder="e.g. \\nUser:, <|end|>, ---",
                    info="Model stops generating when it hits any of these 💕",
                )

                # Heart Divider
                gr.HTML('<div class="heart-divider">♥ ♥ ♥</div>')

                # ---- Prompt Engineering Tips ----
                gr.HTML("""
                <div class="neon-border" style="margin-bottom: 12px;">
                    <h3 style="margin:0 0 6px 0; color:#ff6b9d; font-family:'Quicksand',sans-serif;">
                        📚 Prompt Engineering Tips
                    </h3>
                    <p style="margin:0; color:#7a6578; font-size:0.85em;">
                        Learn what each parameter does 💖
                    </p>
                </div>
                """)
                tip_dropdown = gr.Dropdown(
                    choices=list(PROMPT_ENG_TIPS.keys()),
                    value=list(PROMPT_ENG_TIPS.keys())[0],
                    label="Choose a topic",
                )
                tip_content = gr.Markdown(
                    value=PROMPT_ENG_TIPS[list(PROMPT_ENG_TIPS.keys())[0]],
                )

                tip_dropdown.change(
                    fn=lambda topic: PROMPT_ENG_TIPS.get(topic, ""),
                    inputs=[tip_dropdown],
                    outputs=[tip_content],
                )

        # ---- Bottom Heart Decoration ----
        gr.HTML("""
        <div style="text-align: center; padding: 16px 0 8px 0;">
            <div class="heart-divider" style="font-size: 22px; letter-spacing: 18px; opacity: 0.3;">
                ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥ ♥
            </div>
            <p style="color: #7a6578; font-size: 0.8em; font-family: 'Quicksand', sans-serif;">
                Prompt Engineering Lab 💖 Experiment · Learn · Master 💕
            </p>
        </div>
        """)

        # ---- Wire up all the events ----

        # Chat send
        all_chat_inputs = [
            chat_input, chatbot, api_url, system_prompt,
            max_tokens, temperature, top_p, top_k,
            repeat_penalty, frequency_penalty, presence_penalty,
            n_ctx, stop_sequences,
        ]

        chat_send_btn.click(
            fn=send_chat,
            inputs=all_chat_inputs,
            outputs=[chat_input, chatbot, chat_debug],
        )

        chat_input.submit(
            fn=send_chat,
            inputs=all_chat_inputs,
            outputs=[chat_input, chatbot, chat_debug],
        )

        # Clear chat
        clear_chat_btn.click(
            fn=lambda: ([], ""),
            outputs=[chatbot, chat_debug],
        )

        # Completion
        all_completion_inputs = [
            completion_prompt, api_url, system_prompt,
            max_tokens, temperature, top_p, top_k,
            repeat_penalty, frequency_penalty, presence_penalty,
            n_ctx, stop_sequences,
        ]

        completion_btn.click(
            fn=send_completion,
            inputs=all_completion_inputs,
            outputs=[completion_output, completion_debug],
        )

        # Presets
        apply_preset_btn.click(
            fn=apply_preset,
            inputs=[preset_dropdown],
            outputs=[
                system_prompt, max_tokens, temperature, top_p, top_k,
                repeat_penalty, frequency_penalty, presence_penalty, stop_sequences,
            ],
        )

    return demo


# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    demo = build_ui()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
    )







# gradio.exceptions.Error: "Data incompatible with messages format. Each message should be a dictionary with 'role' and 'content' keys or a ChatMessage object."