import torch
import torch.nn as nn
import pandas as pd
from sklearn.model_selection import train_test_split
from fastapi import FastAPI, Request
from pydantic import BaseModel
from typing import Optional
import uvicorn
import os

# --- Tokenizer ---
class ScratchTokenizer:
    def __init__(self):
        self.word2idx = {"<PAD>": 0, "<SOS>": 1, "<EOS>": 2, "<UNK>": 3}
        self.idx2word = {0: "<PAD>", 1: "<SOS>", 2: "<EOS>", 3: "<UNK>"}
        self.vocab_size = 4

    def build_vocab(self, texts):
        for text in texts:
            for word in text.split():
                if word not in self.word2idx:
                    self.word2idx[word] = self.vocab_size
                    self.idx2word[self.vocab_size] = word
                    self.vocab_size += 1

    def encode(self, text, max_len=200):
        tokens = [self.word2idx.get(word, 3) for word in text.split()]
        tokens = [1] + tokens[:max_len - 2] + [2]
        return tokens + [0] * (max_len - len(tokens))

    def decode(self, tokens):
        return " ".join([self.idx2word.get(idx, "<UNK>") for idx in tokens if idx > 0])

# --- Load and Prepare Data ---
url = "https://drive.google.com/uc?id=1RCZShB5ohy1HdU-mogcP16TbeVv9txpY"
df = pd.read_csv(url)
train_data, _ = train_test_split(df, test_size=0.2, random_state=42)

tokenizer = ScratchTokenizer()
tokenizer.build_vocab(train_data["instruction"].tolist() + train_data["response"].tolist())

# --- Model ---
class GPTModel(nn.Module):
    def __init__(self, vocab_size, embed_size=256, num_heads=8, num_layers=6, max_len=200):
        super(GPTModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_size)
        self.pos_embedding = nn.Parameter(torch.randn(1, max_len, embed_size))
        self.transformer = nn.TransformerDecoder(
            nn.TransformerDecoderLayer(d_model=embed_size, nhead=num_heads),
            num_layers=num_layers
        )
        self.fc_out = nn.Linear(embed_size, vocab_size)

    def forward(self, src, tgt):
        src_emb = self.embedding(src) + self.pos_embedding[:, :src.size(1), :]
        tgt_emb = self.embedding(tgt) + self.pos_embedding[:, :tgt.size(1), :]
        tgt_mask = nn.Transformer.generate_square_subsequent_mask(tgt.size(1)).to(tgt.device)
        output = self.transformer(tgt_emb.permute(1, 0, 2), src_emb.permute(1, 0, 2), tgt_mask=tgt_mask)
        return self.fc_out(output.permute(1, 0, 2))

# --- Load Model ---
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = GPTModel(tokenizer.vocab_size).to(device)

def load_model(model, path="gpt_model.pth"):
    if os.path.exists(path):
        model.load_state_dict(torch.load(path, map_location=device))
        model.eval()
        print("Model loaded successfully.")
    else:
        print("Model file not found!")

load_model(model)

# --- Inference ---
def generate_response(query, max_length=200):
    model.eval()
    with torch.no_grad():
        src = torch.tensor(tokenizer.encode(query)).unsqueeze(0).to(device)
        tgt = torch.tensor([[1]]).to(device)  # <SOS>
        for _ in range(max_length):
            output = model(src, tgt)
            next_word = output.argmax(-1)[:, -1].unsqueeze(1)
            tgt = torch.cat([tgt, next_word], dim=1)
            if next_word.item() == 2:  # <EOS>
                break
    return tokenizer.decode(tgt.squeeze(0).tolist())

# --- FastAPI App ---
app = FastAPI()

class QueryRequest(BaseModel):
    query: str

@app.get("/")
def root():
    return {"message": "Transformer-based Response Generator API is running!"}

@app.post("/query")
def query_model(data: QueryRequest):
    query = data.query.strip()
    if not query:
        return {"error": "Query cannot be empty"}
    response = generate_response(query)
    return {"query": query, "response": response}