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| import gradio as gr | |
| import numpy as np | |
| import tensorflow as tf | |
| import re | |
| from tree_sitter import Language, Parser | |
| import tree_sitter_languages # Pre-built parsers for multiple languages | |
| from tokenizers import Tokenizer | |
| from tensorflow.keras.preprocessing.sequence import pad_sequences | |
| tokenizer = Tokenizer.from_file("syntax_bpe_tokenizer.json") # New BPE tokenizer | |
| model = tf.keras.models.load_model("crv3.keras") # CNN model | |
| parser = Parser() | |
| parser.set_language(tree_sitter_languages.get_language("java")) | |
| def syntax_aware_tokenize(code): | |
| """Tokenizes Java code using Tree-Sitter (AST-based).""" | |
| tree = parser.parse(bytes(code, "utf8")) | |
| root_node = tree.root_node | |
| tokens = [] | |
| def extract_tokens(node): | |
| """Recursively extracts tokens from AST.""" | |
| if node.child_count == 0: # Leaf node | |
| tokens.append(node.text.decode("utf-8")) | |
| for child in node.children: | |
| extract_tokens(child) | |
| extract_tokens(root_node) | |
| return tokens # Returns structured syntax tokens | |
| def replace_java_comments(code: str) -> str: | |
| """Replaces Java comments with placeholders.""" | |
| code = re.sub(r"//.*", " // ", code) # Replace single-line comments | |
| code = re.sub(r"/\*[\s\S]*?\*/", " /**/ ", code) # Replace multi-line comments | |
| return code.strip() # Preserve indentation and code structure | |
| def tokenize_java_code(code: str, max_length=100): | |
| """ | |
| Tokenizes and pads Java code using AST tokenization + BPE. | |
| Args: | |
| code (str): Java code snippet. | |
| max_length (int): Maximum sequence length. | |
| Returns: | |
| np.array: Tokenized and padded sequence. | |
| """ | |
| cleaned_code = replace_java_comments(code) # Preprocess comments | |
| syntax_tokens = syntax_aware_tokenize(cleaned_code) # Extract AST tokens | |
| encoded = tokenizer.encode(" ".join(syntax_tokens)).ids # Apply BPE | |
| # Pad the sequence | |
| padded_sequence = pad_sequences([encoded], maxlen=max_length, padding="post")[0] | |
| return np.array(padded_sequence).reshape(1, -1) # Ensure correct shape for model | |
| def classify_code(input_text, input_file): | |
| """Classifies Java code readability based on user input.""" | |
| # Load Java file if provided | |
| if input_file is not None: | |
| code = input_file.decode("utf-8") # Read Java file as text | |
| else: | |
| code = input_text # Use text input | |
| if not code.strip(): # Ensure input is not empty | |
| return "Please provide a Java code snippet." | |
| # Tokenize and predict | |
| tokenized_code = tokenize_java_code(code) | |
| prediction = model.predict(tokenized_code)[0][0] | |
| threshold = 0.49 # Adjust threshold for classification | |
| prediction = (prediction > threshold).astype(int) # Convert probability to binary | |
| return "Readable" if prediction == 1 else "Unreadable" | |
| gr.Interface( | |
| fn=classify_code, | |
| inputs=[ | |
| gr.Textbox(lines=10, placeholder="Paste Java code here...", label="Java Code Snippet"), | |
| gr.File(type="binary", label="Upload Java File (.java)") | |
| ], | |
| outputs=gr.Text(label="Readability Classification"), | |
| title="Java Code Readability Classifier (AST + BPE)", | |
| description="Upload a Java file or paste a Java code snippet to check if it's readable or unreadable.", | |
| allow_flagging="never" | |
| ).launch() |