import torch
import torch.nn as nn
import gradio as gr
import os
import pickle

class LayerNorm(nn.Module):
    def __init__(self, emb_dim):
        super().__init__()
        self.eps = 1e-5
        self.scale = nn.Parameter(torch.ones(emb_dim))
        self.shift = nn.Parameter(torch.zeros(emb_dim))

    def forward(self, x):
        mean = x.mean(dim=-1, keepdim=True)
        var = x.var(dim=-1, keepdim=True)
        norm_x = (x - mean) / torch.sqrt(var + self.eps)
        return self.scale * norm_x + self.shift


class GELU(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(torch.sqrt(torch.tensor(2.0 / torch.pi)) * (x + 0.044715 * torch.pow(x, 3))))


class MultiHeadAttention(nn.Module):
    def __init__(self, d_in, d_out, context_length, dropout, num_head, qkv_bias=False):
        super().__init__()
        assert (d_out % num_head == 0)

        self.d_out = d_out
        self.num_head = num_head
        self.head_dim = d_out // num_head

        self.W_query = torch.nn.Linear(d_in, d_out, bias=qkv_bias)
        self.W_key = torch.nn.Linear(d_in, d_out, bias=qkv_bias)
        self.W_value = torch.nn.Linear(d_in, d_out, bias=qkv_bias)

        self.out_proj = torch.nn.Linear(d_out, d_out)
        self.dropout = torch.nn.Dropout(dropout)
        self.register_buffer("mask", torch.triu(torch.ones(context_length, context_length), diagonal=1))

    def forward(self, x):
        b, num_tokens, d_in = x.shape

        keys = self.W_key(x)
        queries = self.W_query(x)
        values = self.W_value(x)

        keys = keys.view(b, num_tokens, self.num_head, self.head_dim)
        values = values.view(b, num_tokens, self.num_head, self.head_dim)
        queries = queries.view(b, num_tokens, self.num_head, self.head_dim)

        keys = keys.transpose(1, 2)
        values = values.transpose(1, 2)
        queries = queries.transpose(1, 2)

        attn_score = queries @ keys.transpose(2, 3)

        mask_bool = self.mask.to(torch.bool)[:num_tokens, :num_tokens]

        attn_score.masked_fill_(mask_bool, -torch.inf)

        attn_weight = torch.softmax(attn_score / keys.shape[-1] ** 0.5, dim=-1)
        attn_weight = self.dropout(attn_weight)

        context_vector = (attn_weight @ values).transpose(1, 2)

        context_vector = context_vector.contiguous().view(b, num_tokens, self.d_out)
        context_vector = self.out_proj(context_vector)

        return context_vector


class FeedForward(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
            GELU(),
            nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"])
        )

    def forward(self, x):
        return self.layers(x)


class TransformerBlock(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.att = MultiHeadAttention(
            d_in=cfg["emb_dim"],
            d_out=cfg["emb_dim"],
            context_length=cfg["context_length"],
            num_head=cfg["n_heads"],
            dropout=cfg.get("drop_rate", 0.0),
            qkv_bias=cfg.get("qkv_bias", False)
        )

        self.ff = FeedForward(cfg)
        self.norm1 = LayerNorm(cfg["emb_dim"])
        self.norm2 = LayerNorm(cfg["emb_dim"])
        self.drop_shortcut = nn.Dropout(cfg.get("drop_rate", 0.0))

    def forward(self, x):
        shortcut = x
        x = self.norm1(x)
        x = self.att(x)
        x = self.drop_shortcut(x)
        x = x + shortcut

        shortcut = x
        x = self.norm2(x)
        x = self.ff(x)
        x = self.drop_shortcut(x)
        x = x + shortcut
        return x


class GPTModel(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
        self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
        self.drop_emb = nn.Dropout(cfg.get("drop_rate", 0.0))

        self.trf_blocks = nn.Sequential(
            *[TransformerBlock(cfg) for _ in range(cfg["n_layers"]) ]
        )

        self.final_norm = LayerNorm(cfg["emb_dim"])
        self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)

    def forward(self, in_idx):
        batch_size, seq_len = in_idx.shape
        tok_embeds = self.tok_emb(in_idx)
        pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
        x = tok_embeds + pos_embeds
        x = self.drop_emb(x)
        x = self.trf_blocks(x)
        x = self.final_norm(x)
        logits = self.out_head(x)
        return logits


model_path = "review_classifier_model.pth"
if not os.path.exists(model_path):
    raise FileNotFoundError(f"{model_path} not found. Please check the path.")

try:
    loaded_full = None
    safe_ctx = getattr(torch.serialization, "safe_globals", None)
    add_safe = getattr(torch.serialization, "add_safe_globals", None)

    if safe_ctx is not None:
        try:
            with torch.serialization.safe_globals([GPTModel]):
                loaded_full = torch.load(model_path, map_location=torch.device("cpu"), weights_only=False)
        except Exception:
            loaded_full = None
    elif add_safe is not None:
        try:
            # older helper: register globally then load
            torch.serialization.add_safe_globals([GPTModel])
            loaded_full = torch.load(model_path, map_location=torch.device("cpu"), weights_only=False)
        except Exception:
            loaded_full = None
    else:
        # If neither helper exists, try loading with weights_only=False (may execute code during unpickle).
        try:
            loaded_full = torch.load(model_path, map_location=torch.device("cpu"), weights_only=False)
        except Exception:
            loaded_full = None

    if loaded_full is not None and hasattr(loaded_full, "state_dict") and not isinstance(loaded_full, dict):
        model = loaded_full
        print(f"Loaded full model object from {model_path}")
    else:
        state = None
        try:
            state = torch.load(model_path, map_location=torch.device("cpu"), weights_only=True)
        except Exception:
            try:
                if safe_ctx is not None:
                    with torch.serialization.safe_globals([GPTModel]):
                        tmp = torch.load(model_path, map_location=torch.device("cpu"), weights_only=False)
                elif add_safe is not None:
                    torch.serialization.add_safe_globals([GPTModel])
                    tmp = torch.load(model_path, map_location=torch.device("cpu"), weights_only=False)
                else:
                    tmp = torch.load(model_path, map_location=torch.device("cpu"), weights_only=False)

                if hasattr(tmp, "state_dict"):
                    state = tmp.state_dict()
                else:
                    state = tmp
            except Exception as e:
                raise RuntimeError(f"Unable to load checkpoint as full model or weights-only. Last error: {e}")

        if isinstance(state, dict):
            print("Attempting to load checkpoint state into a GPTModel instance...")
            BASE_CONFIG = {
                "vocab_size": 50257,
                "context_length": 1024,
                "drop_rate": 0.0,
                "qkv_bias": True,
                "emb_dim": 768,
                "n_layers": 12,
                "n_heads": 12,
            }
            model = GPTModel(BASE_CONFIG)

            if "model_state_dict" in state:
                state_dict = state["model_state_dict"]
            elif "state_dict" in state:
                state_dict = state["state_dict"]
            else:
                state_dict = state

            model.load_state_dict(state_dict, strict=False)
            print("Loaded state_dict into GPTModel instance (non-strict).")
        else:
            raise RuntimeError("Unrecognized checkpoint format and unable to construct model from checkpoint.")
except Exception as e:
    raise RuntimeError(f"Failed to load model checkpoint: {e}")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
print(f"Model loaded and moved to {device}")

tokenizer_path = "tokenizer.pkl"
if os.path.exists(tokenizer_path):
    with open(tokenizer_path, "rb") as f:
        tokenizer = pickle.load(f)
    print(f"Tokenizer loaded from {tokenizer_path}")
else:
    raise FileNotFoundError(f"{tokenizer_path} not found. Please check the path.")

MAX_SEQUENCE_LENGTH = 120

def classify_review(text, model, tokenizer_obj, device, max_length=MAX_SEQUENCE_LENGTH, pad_token_id=50256):
    model.eval()
    input_ids = tokenizer_obj.encode(text)
    input_ids = input_ids[:max_length] + [pad_token_id] * (max_length - len(input_ids))
    input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0)
    with torch.no_grad():
        logits = model(input_tensor)[:, -1, :]
    predicted_label = torch.argmax(logits, dim=-1).item()
    return "spam" if predicted_label == 1 else "not spam"

def chatbot_classify(message, history):
    result = classify_review(
        message,
        model,
        tokenizer,
        device,
        max_length=MAX_SEQUENCE_LENGTH
    )
    return result

print("Launching Gradio interface...")

iface = gr.ChatInterface(
    chatbot_classify,
    title="📬 Spam Detection System",
    description="Enter an SMS message below...",
    theme="compact",  
    css="""
        /* Customize chat bubble colors */
        .chatbot-message {
            background-color: #e0f7fa; /* Light cyan */
            color: #006064;           /* Dark teal text */
            font-weight: 600;
            border-radius: 12px;
            padding: 12px;
        }
        .user-message {
            background-color: #c8e6c9; /* Light green */
            color: #1b5e20;            /* Dark green text */
            font-weight: 600;
            border-radius: 12px;
            padding: 12px;
        }
        .chat-ending-message {
            font-style: italic;
            color: #555;
        }
    """,
)

ICON_CDN = "https://img.icons8.com/color/48/mail-envelope.png"

custom_head_html = f"""
    <link rel="icon" href="{ICON_CDN}" type="image/x-icon">
"""

iface.launch(
    share=True,
    favicon_path=ICON_CDN
)