Add user interface to ch06 and ch07 (#366)

* Add user interface to ch06 and ch07

* pep8

* fix url

ch06/04_user_interface/app.py

@@ -0,0 +1,80 @@
+# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
+# Source for "Build a Large Language Model From Scratch"
+#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
+# Code: https://github.com/rasbt/LLMs-from-scratch
+
+from pathlib import Path
+import sys
+
+import tiktoken
+import torch
+import chainlit
+
+from previous_chapters import (
+    classify_review,
+    GPTModel
+)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def get_model_and_tokenizer():
+    """
+    Code to load finetuned GPT-2 model generated in chapter 6.
+    This requires that you run the code in chapter 6 first, which generates the necessary model.pth file.
+    """
+
+    GPT_CONFIG_124M = {
+        "vocab_size": 50257,     # Vocabulary size
+        "context_length": 1024,  # Context length
+        "emb_dim": 768,          # Embedding dimension
+        "n_heads": 12,           # Number of attention heads
+        "n_layers": 12,          # Number of layers
+        "drop_rate": 0.1,        # Dropout rate
+        "qkv_bias": True         # Query-key-value bias
+    }
+
+    tokenizer = tiktoken.get_encoding("gpt2")
+
+    model_path = Path("..") / "01_main-chapter-code" / "review_classifier.pth"
+    if not model_path.exists():
+        print(
+            f"Could not find the {model_path} file. Please run the chapter 6 code"
+            " (ch06.ipynb) to generate the review_classifier.pth file."
+        )
+        sys.exit()
+
+    # Instantiate model
+    model = GPTModel(GPT_CONFIG_124M)
+
+    # Convert model to classifier as in section 6.5 in ch06.ipynb
+    num_classes = 2
+    model.out_head = torch.nn.Linear(in_features=GPT_CONFIG_124M["emb_dim"], out_features=num_classes)
+
+    # Then load model weights
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    checkpoint = torch.load(model_path, map_location=device, weights_only=True)
+    model.load_state_dict(checkpoint)
+    model.to(device)
+    model.eval()
+
+    return tokenizer, model
+
+
+# Obtain the necessary tokenizer and model files for the chainlit function below
+tokenizer, model = get_model_and_tokenizer()
+
+
+@chainlit.on_message
+async def main(message: chainlit.Message):
+    """
+    The main Chainlit function.
+    """
+    user_input = message.content
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    label = classify_review(user_input, model, tokenizer, device, max_length=120)
+
+    await chainlit.Message(
+        content=f"{label}",  # This returns the model response to the interface
+    ).send()