app.py

import gradio as gr
import torch
import torch.nn as nn
import sentencepiece as spm
import math

# Transformer class definitions (unchanged)
class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        assert d_model % num_heads == 0
        self.d_model = d_model
        self.num_heads = num_heads
        self.d_k = d_model // num_heads
        self.W_q = nn.Linear(d_model, d_model)
        self.W_k = nn.Linear(d_model, d_model)
        self.W_v = nn.Linear(d_model, d_model)
        self.W_o = nn.Linear(d_model, d_model)
        
    def scaled_dot_product_attention(self, Q, K, V, mask=None):
        attn_scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)
        if mask is not None:
            attn_scores = attn_scores.masked_fill(mask == 0, -1e9)
        attn_probs = torch.softmax(attn_scores, dim=-1)
        output = torch.matmul(attn_probs, V)
        return output
        
    def split_heads(self, x):
        batch_size, seq_length, d_model = x.size()
        return x.view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2)
        
    def combine_heads(self, x):
        batch_size, _, seq_length, d_k = x.size()
        return x.transpose(1, 2).contiguous().view(batch_size, seq_length, self.d_model)
        
    def forward(self, Q, K, V, mask=None):
        Q = self.split_heads(self.W_q(Q))
        K = self.split_heads(self.W_k(K))
        V = self.split_heads(self.W_v(V))
        attn_output = self.scaled_dot_product_attention(Q, K, V, mask)
        output = self.W_o(self.combine_heads(attn_output))
        return output

class PositionWiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff):
        super(PositionWiseFeedForward, self).__init__()
        self.fc1 = nn.Linear(d_model, d_ff)
        self.fc2 = nn.Linear(d_ff, d_model)
        self.relu = nn.ReLU()

    def forward(self, x):
        return self.fc2(self.relu(self.fc1(x)))

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_seq_length):
        super(PositionalEncoding, self).__init__()
        pe = torch.zeros(max_seq_length, d_model)
        position = torch.arange(0, max_seq_length, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        self.register_buffer('pe', pe.unsqueeze(0))
        
    def forward(self, x):
        return x + self.pe[:, :x.size(1)]

class EncoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff, dropout):
        super(EncoderLayer, self).__init__()
        self.self_attn = MultiHeadAttention(d_model, num_heads)
        self.feed_forward = PositionWiseFeedForward(d_model, d_ff)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x, mask):
        attn_output = self.self_attn(x, x, x, mask)
        x = self.norm1(x + self.dropout(attn_output))
        ff_output = self.feed_forward(x)
        x = self.norm2(x + self.dropout(ff_output))
        return x

class DecoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff, dropout):
        super(DecoderLayer, self).__init__()
        self.self_attn = MultiHeadAttention(d_model, num_heads)
        self.cross_attn = MultiHeadAttention(d_model, num_heads)
        self.feed_forward = PositionWiseFeedForward(d_model, d_ff)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x, enc_output, src_mask, tgt_mask):
        attn_output = self.self_attn(x, x, x, tgt_mask)
        x = self.norm1(x + self.dropout(attn_output))
        attn_output = self.cross_attn(x, enc_output, enc_output, src_mask)
        x = self.norm2(x + self.dropout(attn_output))
        ff_output = self.feed_forward(x)
        x = self.norm3(x + self.dropout(ff_output))
        return x

class Transformer(nn.Module):
    def __init__(self, src_vocab_size, tgt_vocab_size, d_model, num_heads, num_layers, d_ff, max_seq_length, dropout):
        super(Transformer, self).__init__()
        self.encoder_embedding = nn.Embedding(src_vocab_size, d_model)
        self.decoder_embedding = nn.Embedding(tgt_vocab_size, d_model)
        self.positional_encoding = PositionalEncoding(d_model, max_seq_length)
        self.encoder_layers = nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
        self.decoder_layers = nn.ModuleList([DecoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
        self.fc = nn.Linear(d_model, tgt_vocab_size)
        self.dropout = nn.Dropout(dropout)

    def generate_mask(self, src, tgt):
        src_mask = (src != 0).unsqueeze(1).unsqueeze(2)
        tgt_mask = (tgt != 0).unsqueeze(1).unsqueeze(3)
        seq_length = tgt.size(1)
        nopeak_mask = (1 - torch.triu(torch.ones(1, seq_length, seq_length), diagonal=1)).bool()
        tgt_mask = tgt_mask & nopeak_mask
        return src_mask, tgt_mask

    def forward(self, src, tgt):
        src_mask, tgt_mask = self.generate_mask(src, tgt)
        src_embedded = self.dropout(self.positional_encoding(self.encoder_embedding(src)))
        tgt_embedded = self.dropout(self.positional_encoding(self.decoder_embedding(tgt)))
        enc_output = src_embedded
        for enc_layer in self.encoder_layers:
            enc_output = enc_layer(enc_output, src_mask)
        dec_output = tgt_embedded
        for dec_layer in self.decoder_layers:
            dec_output = dec_layer(dec_output, enc_output, src_mask, tgt_mask)
        output = self.fc(dec_output)
        return output

# Device setup
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load tokenizers
sp_code = spm.SentencePieceProcessor(model_file="code_tokenizer.model")      # C++ tokenizer for input
sp_pseudo = spm.SentencePieceProcessor(model_file="pseudocode_tokenizer.model")  # Pseudocode tokenizer for output

# Load the model trained for C++ to pseudocode
model_path = "c2p.pth"  # Ensure this is the correct model for C++ to pseudocode
model = torch.load(model_path, map_location=device, weights_only=False)
model.eval()
model = model.to(device)

# Function to generate pseudocode from C++ code with streaming
def generate_pseudocode(cpp_code, max_len=500):
    model.eval()
    src = torch.tensor([sp_code.encode_as_ids(cpp_code)], dtype=torch.long, device=device)  # Tokenize C++ input
    tgt = torch.tensor([[2]], dtype=torch.long, device=device)  # <BOS> token (ID=2)
    
    generated_tokens = [2]  # Start with <BOS>
    eos_id = sp_pseudo.eos_id()  # Dynamically get <EOS> ID from tokenizer
    print(f"Input C++ tokens: {sp_code.encode_as_ids(cpp_code)}")  # Debug input
    print(f"Using EOS ID: {eos_id}")  # Debug EOS ID
    
    with torch.no_grad():
        for i in range(max_len):
            output = model(src, tgt)
            next_token = output[:, -1, :].argmax(-1).item()
            generated_tokens.append(next_token)
            tgt = torch.cat([tgt, torch.tensor([[next_token]], device=device)], dim=1)
            response = sp_pseudo.decode_ids(generated_tokens)  # Decode to pseudocode
            print(f"Step {i}: Next token = {next_token}, Partial output = {response}")  # Debug step
            yield response  # Yield partial output for streaming
            if next_token == eos_id:  # Stop at <EOS>
                print("EOS detected, stopping generation.")
                break
        print("Generation complete or max length reached.")
    
    yield response  # Final output

# Gradio interface function with streaming
def generate_output(cpp_code):
    for response in generate_pseudocode(cpp_code, max_len=500):
        yield response

# Gradio UI setup
with gr.Blocks(title="C++ to Pseudocode Transformer") as demo:
    gr.Markdown("## C++ to Pseudocode Converter")
    gr.Markdown("Enter C++ code below to generate pseudocode.")
    cpp_input = gr.Textbox(
        label="C++ Code",
        placeholder="e.g., 'int main() { int n; cin >> n; }'",
        lines=5
    )
    generate_btn = gr.Button("Generate", variant="primary", elem_classes="btn-blue")
    pseudocode_output = gr.Textbox(
        label="Generated Pseudocode",
        lines=5
    )
    
    generate_btn.click(
        fn=generate_output,
        inputs=[cpp_input],
        outputs=pseudocode_output
    )

demo.launch()

# Custom CSS
demo.css = """
.btn-blue {
    background-color: #007bff;
    color: white;
    border: none;
}
.btn-blue:hover {
    background-color: #0056b3;
}
"""