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app.py

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+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, Response, stream_with_context
+from sklearn.model_selection import train_test_split
+import os
+import time
+import json
+
+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())
+
+# 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!")
+
+
+def generate_response_stream(model, 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_token = output[:, -1, :].argmax(dim=-1, keepdim=True)
+            tgt = torch.cat([tgt, next_token], dim=1)
+            
+            # Get the current word
+            current_word = tokenizer.idx2word.get(next_token.item(), "<UNK>")
+            if current_word != "<PAD>":
+                yield current_word + " "
+            
+            if next_token.item() == 2:  # <EOS>
+                break
+
+# Flask App
+app = Flask(__name__)
+
+@app.route("/")
+def home():
+    return {"message": "Streaming Transformer-based Response Generator API is running!"}
+
+@app.route("/intent")
+def intents():
+    return jsonify({"intents": list(set(df['intent'].dropna()))})
+
+@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
+    
+    def generate():
+        start = time.time()
+        for word in generate_response_stream(model, query):
+            response_data = {
+                "word": word,
+                "timestamp": time.time() - start
+            }
+            yield f"data: {json.dumps(response_data)}\n\n"
+    
+    return Response(stream_with_context(generate()), mimetype='text/event-stream')
+
+if __name__ == "__main__":
+    load_model(model)
+    app.run(host="0.0.0.0", port=7860) 

requirements.txt

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+torch>=2.0.0
+pandas>=1.3.0
+scikit-learn>=1.0
+flask>=2.0
+flask-cors>=3.0