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25faba3 84d4bbc 25faba3 84d4bbc 25faba3 84d4bbc 25faba3 84d4bbc 25faba3 84d4bbc 25faba3 84d4bbc | 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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | import os
import sys
# Fix macOS MPS issues - MUST be before ANY torch/transformers imports
if sys.platform == "darwin": # macOS
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["PYTORCH_ENABLE_MPS"] = "0" # Explicitly disable MPS
import gradio as gr
import torch
# Disable MPS after torch import
if sys.platform == "darwin":
try:
torch.backends.mps.enabled = False
torch.set_default_device("cpu")
except:
pass
from ai_text_detector.models import DetectorModel
from ai_text_detector.datasets import DatasetLoader
# Initialize model and tokenizer
model = None
tokenizer = None
def load_model():
"""Load the trained model if it exists, otherwise use a base model for demo"""
global model, tokenizer
model_path = "models/ai_detector"
# Check if model directory exists AND has model files
has_model = False
if os.path.exists(model_path):
# Check for required model files
required_files = ["config.json", "pytorch_model.bin"]
has_model = all(os.path.exists(os.path.join(model_path, f)) for f in required_files)
if has_model:
try:
print(f"Loading trained model from {model_path}")
model = DetectorModel.load(model_path)
tokenizer = model.tokenizer
except Exception as e:
print(f"Failed to load model: {e}")
print("Using Desklib pre-trained model instead.")
model = DetectorModel("desklib/ai-text-detector-v1.01", use_desklib=True)
tokenizer = model.tokenizer
else:
print("No trained model found. Using Desklib pre-trained AI detector model.")
# Use Desklib pre-trained model (no training needed!)
model = DetectorModel("desklib/ai-text-detector-v1.01", use_desklib=True)
tokenizer = model.tokenizer
# Load model lazily (on first use) to avoid startup issues
_model_loaded = False
def ensure_model_loaded():
"""Load model if not already loaded"""
global model, tokenizer, _model_loaded
if not _model_loaded:
load_model()
_model_loaded = True
def detect_text(text):
"""Detect if text is AI-generated or human-written"""
global model, tokenizer
# Load model on first use
ensure_model_loaded()
if not text.strip():
return "Please enter some text to analyze."
try:
# Use the model's predict method
ai_prob, predicted_label = model.predict(text, max_length=768, threshold=0.5)
# Determine prediction
if predicted_label == 1:
label = "🤖 AI-generated"
confidence = ai_prob
else:
label = "🧑 Human-written"
confidence = 1 - ai_prob # Human probability is 1 - AI probability
return f"{label} (confidence: {confidence:.1%})"
except Exception as e:
return f"Error processing text: {str(e)}"
# Create Gradio interface (model will load on first detection)
print("Starting Gradio app... Model will load on first use.")
with gr.Blocks(title="AI Text Detector", theme=gr.themes.Soft()) as app:
gr.Markdown("# 🔍 AI Text Detector")
gr.Markdown("Paste any text below to detect if it was written by AI or a human.")
with gr.Row():
with gr.Column():
text_input = gr.Textbox(
label="Text to analyze",
placeholder="Enter text here...",
lines=5,
max_lines=10
)
detect_btn = gr.Button("🔍 Detect", variant="primary")
with gr.Column():
result_output = gr.Textbox(
label="Prediction",
interactive=False,
lines=3
)
# Connect the button to the function
detect_btn.click(
fn=detect_text,
inputs=text_input,
outputs=result_output
)
# Also detect on Enter key
text_input.submit(
fn=detect_text,
inputs=text_input,
outputs=result_output
)
# Add some example texts
gr.Markdown("### 💡 Try these examples:")
examples = [
"The sunset painted the sky in hues of crimson and gold, casting long shadows across the meadow.",
"The quantum tensor optimization algorithm significantly reduced inference latency by 23.7%.",
"I went to the store yesterday and bought some milk and bread.",
"The implementation leverages advanced neural architecture search techniques to optimize model performance."
]
gr.Examples(
examples=examples,
inputs=text_input,
outputs=result_output,
fn=detect_text,
cache_examples=False
)
if __name__ == "__main__":
app.launch(share=True, server_name="0.0.0.0", server_port=7860)
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