File size: 1,788 Bytes
f7601c8 9ac8247 f7601c8 9ac8247 f7601c8 9ac8247 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
import gradio as gr
import numpy as np
import tensorflow as tf
from tokenizers import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Load trained tokenizer and model
tokenizer = Tokenizer.from_file("cr_tokenizer.json")
model = tf.keras.models.load_model("crv3.keras")
# Tokenization function
def tokenize_java_code(code: str, max_length=100):
"""Tokenizes and pads Java code for model input."""
encoded = tokenizer.encode(code).ids
padded_sequence = pad_sequences([encoded], maxlen=max_length, padding="post")[0]
return np.array(padded_sequence).reshape(1, -1) # Ensure correct shape for model
# Prediction function
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.read().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]
# Convert to readable/unreadable
return "Readable" if prediction > 0.5 else "Unreadable"
# Create Gradio interface
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 Prediction"),
title="Java Code Readability Classifier",
description="Upload a Java file or paste a Java code snippet to check if it's readable or unreadable.",
allow_flagging="never"
).launch() |