main.py

import torch
import torch.nn as nn
from torch.nn import functional as F
import pickle
from fastapi import FastAPI
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware

# --- Hyperparameters (Must match your model.py) ---
n_embd = 512
n_head = 8
n_layer = 12
dropout = 0.2
block_size = 512
device = 'cuda' if torch.cuda.is_available() else 'cpu'

# --- Model Architecture (Copied from your model.py) ---
class Head(nn.Module):
    def __init__(self, head_size):
        super().__init__()
        self.key = nn.Linear(n_embd, head_size, bias=False)
        self.query = nn.Linear(n_embd, head_size, bias=False)
        self.value = nn.Linear(n_embd, head_size, bias=False)
        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
        self.dropout = nn.Dropout(dropout)
    def forward(self, x):
        B,T,C = x.shape
        k, q, v = self.key(x), self.query(x), self.value(x)
        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
        wei = F.softmax(wei, dim=-1)
        return self.dropout(wei) @ v

class MultiHeadAttention(nn.Module):
    def __init__(self, num_heads, head_size):
        super().__init__()
        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
        self.proj = nn.Linear(head_size * num_heads, n_embd)
        self.dropout = nn.Dropout(dropout)
    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1)
        return self.dropout(self.proj(out))

class FeedFoward(nn.Module):
    def __init__(self, n_embd):
        super().__init__()
        self.net = nn.Sequential(nn.Linear(n_embd, 4 * n_embd), nn.ReLU(), nn.Linear(4 * n_embd, n_embd), nn.Dropout(dropout))
    def forward(self, x): return self.net(x)

class Block(nn.Module):
    def __init__(self, n_embd, n_head):
        super().__init__()
        head_size = n_embd // n_head
        self.sa, self.ffwd = MultiHeadAttention(n_head, head_size), FeedFoward(n_embd)
        self.ln1, self.ln2 = nn.LayerNorm(n_embd), nn.LayerNorm(n_embd)
    def forward(self, x):
        x = x + self.sa(self.ln1(x))
        return x + self.ffwd(self.ln2(x))

class GPTLanguageModel(nn.Module):
    def __init__(self, vocab_size):
        super().__init__()
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd)
        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
        self.ln_f, self.lm_head = nn.LayerNorm(n_embd), nn.Linear(n_embd, vocab_size)
    def forward(self, idx, targets=None):
        B, T = idx.shape
        tok_emb = self.token_embedding_table(idx)
        pos_emb = self.position_embedding_table(torch.arange(T, device=device))
        x = self.ln_f(self.blocks(tok_emb + pos_emb))
        logits = self.lm_head(x)
        return logits, None
    def generate(self, idx, max_new_tokens):
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -block_size:]
            logits, _ = self(idx_cond)
            probs = F.softmax(logits[:, -1, :], dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
        return idx

# --- Server Logic ---
app = FastAPI()
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])

# Load Metadata and Model
with open('meta.pkl', 'rb') as f:
    meta = pickle.load(f)
stoi, itos = meta['stoi'], meta['itos']
encode = lambda s: [stoi[c] for c in s if c in stoi] # Filter unknown chars
decode = lambda l: ''.join([itos[i] for i in l])

model = GPTLanguageModel(meta['vocab_size'])
# Load just the weights (state_dict) or the whole model
try:
    checkpoint = torch.load("finetuned_model.pt", map_location=device)
    if isinstance(checkpoint, dict):
        model.load_state_dict(checkpoint)
    else:
        model = checkpoint
    model.to(device)
    model.eval()
    print("Model loaded successfully!")
except Exception as e:
    print(f"Error: {e}")

class ChatRequest(BaseModel):
    prompt: str

@app.post("/chat")
async def chat(request: ChatRequest):
    # Wrap the prompt to force a dialogue structure
    context = f"User: {request.prompt}\nChen Bot:"
    input_ids = torch.tensor(encode(context), dtype=torch.long, device=device).unsqueeze(0)
    
    with torch.no_grad():
        output_ids = model.generate(input_ids, max_new_tokens=200)[0].tolist()
    
    full_text = decode(output_ids)
    
    # Extract response: strictly what comes after our manually injected context
    reply_start_index = len(context)
    raw_reply = full_text[reply_start_index:]
    
    # Stop generation if the model tries to start a new "User:" turn
    clean_reply = raw_reply.split("User:")[0].strip()
    
    return {"reply": clean_reply}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)