Update app.py

app.py

@@ -61,7 +61,7 @@ class TextDataset(Dataset):
 # 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_()
+        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(
@@ -120,127 +120,4 @@ def query_model():
     response = generate_response(model, query)
     return jsonify({"query": query, "response": response})
 
-# DO NOT ADD app.run()
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import pandas as pd
-from torch.utils.data import Dataset, DataLoader
-from flask import Flask, request, jsonify
-from sklearn.model_selection import train_test_split
-import os
-
-# Load data
-url = "https://drive.google.com/uc?id=1RCZShB5ohy1HdU-mogcP16TbeVv9txpY"
-df = pd.read_csv(url)
-
-# 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])
-
-# Train-Test Split
-train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)
-
-# Initialize Tokenizer
-tokenizer = ScratchTokenizer()
-tokenizer.build_vocab(train_data["instruction"].tolist() + train_data["response"].tolist())
-
-# Dataset Class
-class TextDataset(Dataset):
-    def __init__(self, data, tokenizer, max_len=200):
-        self.data = data
-        self.tokenizer = tokenizer
-        self.max_len = max_len
-
-    def _len_(self):
-        return len(self.data)
-
-    def _getitem_(self, idx):
-        src_text = self.data.iloc[idx]["instruction"]
-        tgt_text = self.data.iloc[idx]["response"]
-        src = torch.tensor(self.tokenizer.encode(src_text), dtype=torch.long)
-        tgt = torch.tensor(self.tokenizer.encode(tgt_text), dtype=torch.long)
-        return src, tgt
-
-# 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)
-
-# Generate Response
-def generate_response(model, query, max_length=200):
-    model.eval()
-    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())
-
-# Flask App
-app = Flask(_name_)
-
-@app.route("/")
-def home():
-    return {"message": "Transformer-based Response Generator API is running!"}
-
-@app.route("/query", methods=["POST"])
-def query_model():
-    data = request.get_json()
-    query = data.get("query", "")
-    if not query:
-        return jsonify({"error": "Query cannot be empty"}), 400
-    response = generate_response(model, query)
-    return jsonify({"query": query, "response": response})
-
-# DO NOT ADD app.run()
+# DO NOT ADD app.run()