app.py

import gradio as gr
import torch
import torch.nn as nn
from torch.nn import functional as F
import json
import os

# --- Model Hyperparameters (same as before) ---
batch_size = 32
block_size = 8
n_embd = 32
n_head = 4
n_layer = 4
dropout = 0.0
max_iters = 3000
eval_interval = 300
learning_rate = 1e-2
eval_iters = 200

# --- Data Preparation & Vocabulary Creation ---
file_path = 'dataset.jsonl'
corpus = ""
try:
    with open(file_path, 'r') as f:
        for line in f:
            data_point = json.loads(line)
            corpus += data_point['header'] + '\n' + data_point['formal_statement'] + '\n'
except FileNotFoundError:
    print(f"Error: The file '{file_path}' was not found.")
    exit()
except (json.JSONDecodeError, KeyError):
    print(f"Error: There was a problem parsing a line in '{file_path}'. Check for malformed JSON or missing keys.")
    exit()

if not corpus:
    print("Error: The corpus is empty.")
    exit()

chars = sorted(list(set(corpus)))
vocab_size = len(chars)
stoi = {ch: i for i, ch in enumerate(chars)}
itos = {i: ch for i, ch in enumerate(chars)}
encode = lambda s: [stoi.get(c, 0) for c in s]
decode = lambda l: ''.join([itos[i] for i in l])

device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Split the data for training and validation
data = torch.tensor(encode(corpus), dtype=torch.long)
n = int(0.9 * len(data))
train_data = data[:n]
val_data = data[n:]

def get_batch(split):
    data = train_data if split == 'train' else val_data
    ix = torch.randint(len(data) - block_size, (batch_size,))
    x = torch.stack([data[i:i + block_size] for i in ix])
    y = torch.stack([data[i + 1:i + block_size + 1] for i in ix])
    x, y = x.to(device), y.to(device)
    return x, y

# --- Model Definition (same as before) ---
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 = self.key(x)
        q = self.query(x)
        wei = q @ k.transpose(-2, -1) * C**-0.5
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
        wei = F.softmax(wei, dim=-1)
        self.dropout(wei)
        v = self.value(x)
        out = wei @ v
        return out

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(num_heads * head_size, n_embd)
        self.dropout = nn.Dropout(dropout)
    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1)
        out = self.dropout(self.proj(out))
        return 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 TransformerBlock(nn.Module):
    def __init__(self, n_embd, n_head):
        super().__init__()
        head_size = n_embd // n_head
        self.sa = MultiHeadAttention(n_head, head_size)
        self.ffwd = FeedFoward(n_embd)
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)
    def forward(self, x):
        x = x + self.sa(self.ln1(x))
        x = x + self.ffwd(self.ln2(x))
        return x

class LanguageModel(nn.Module):
    def __init__(self, vocab_size, block_size, n_embd, n_head, n_layer, dropout):
        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(*[TransformerBlock(n_embd, n_head) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd)
        self.lm_head = nn.Linear(n_embd, vocab_size)
        self.block_size = block_size
        self.vocab_size = 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=idx.device))
        x = tok_emb + pos_emb
        x = self.blocks(x)
        x = self.ln_f(x)
        logits = self.lm_head(x)
        loss = None
        if targets is not None:
            B, T, C = logits.shape
            logits = logits.view(B * T, C)
            targets = targets.view(B * T)
            loss = F.cross_entropy(logits, targets)
        return logits, loss
    def generate(self, idx, max_new_tokens):
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -self.block_size:]
            logits, loss = self(idx_cond)
            logits = logits[:, -1, :]
            probs = F.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
        return idx

# --- Training and Generation ---
model = LanguageModel(vocab_size, block_size, n_embd, n_head, n_layer, dropout)
m = model.to(device)

# --- Check if a trained model exists, otherwise train a new one ---
model_file = 'model.pt'
if os.path.exists(model_file):
    print(f"Loading existing model from {model_file}")
    try:
        model.load_state_dict(torch.load(model_file, map_location=device))
    except RuntimeError as e:
        print(f"Error loading model: {e}")
        print("Model file might be incompatible with current vocabulary. Retraining...")
        # If loading fails, fall through to training logic
        model.train() # Set back to train mode just in case
else:
    print("No trained model found. Starting a new training session...")
    
    # Define a helper function for loss estimation
    @torch.no_grad()
    def estimate_loss():
        out = {}
        model.eval()
        for split in ['train', 'val']:
            losses = torch.zeros(eval_iters)
            for k in range(eval_iters):
                X, Y = get_batch(split)
                logits, loss = model(X, Y)
                losses[k] = loss.item()
            out[split] = losses.mean()
        model.train()
        return out

    # The training loop
    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
    for iter in range(max_iters):
        if iter % eval_interval == 0:
            losses = estimate_loss()
            print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
        xb, yb = get_batch('train')
        logits, loss = model(xb, yb)
        optimizer.zero_grad(set_to_none=True)
        loss.backward()
        optimizer.step()
    
    torch.save(m.state_dict(), model_file)
    print(f"Training complete. Model saved to {model_file}")

model.eval()
model.to(device)

# --- Gradio UI & Inference function ---
def generate_text_chat(message, history):
    prompt = message
    max_new_tokens = 50 
    encoded_prompt = [stoi.get(c, 0) for c in prompt]
    if not encoded_prompt:
        return "Prompt is empty or contains unknown characters."
    context = torch.tensor(encoded_prompt, dtype=torch.long, device=device).unsqueeze(0)
    generated_text_indices = model.generate(context, max_new_tokens=max_new_tokens)
    generated_text = decode(generated_text_indices[0].tolist())
    return generated_text[len(prompt):]

demo = gr.ChatInterface(
    fn=generate_text_chat,
    title="Tiny Language Model Chat",
    description="A simple character-level language model trained in PyTorch, now with a chat interface.",
    chatbot=gr.Chatbot(height="500px"),
    textbox=gr.Textbox(placeholder="Ask me anything...", container=False, scale=7),
    theme="soft",
)

demo.launch()