Spaces:
Sleeping
Sleeping
File size: 3,001 Bytes
8cb40c4 387f42d 8cb40c4 387f42d 8cb40c4 387f42d 8cb40c4 387f42d 8cb40c4 387f42d 8cb40c4 387f42d 8cb40c4 387f42d | 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 | import torch
import gradio as gr
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification
)
MODEL_NAME = "duclo90/results"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
model.eval()
def classify_text(text):
if not text.strip():
return "⚠️ Please enter some text to analyze."
inputs = tokenizer(
text,
return_tensors="pt",
truncation=True,
max_length=512
)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=-1)
score, pred = torch.max(probs, dim=-1)
label = model.config.id2label[pred.item()]
# Format output with icons and styling
icon = "👤" if label.lower() == "human" else "🤖"
result = f"""
### {icon} Classification Result
**Prediction:** {label}-Written Text
"""
return result
# Custom CSS for modern look
custom_css = """
#component-0 {
max-width: 900px;
margin: auto;
padding: 20px;
}
.gradio-container {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
font-family: 'Inter', sans-serif;
}
#title {
text-align: center;
color: white;
font-size: 2.5em;
font-weight: 700;
margin-bottom: 10px;
text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
}
#description {
text-align: center;
color: rgba(255,255,255,0.9);
font-size: 1.1em;
margin-bottom: 30px;
}
.input-textarea, .output-markdown {
border-radius: 15px !important;
border: 2px solid rgba(255,255,255,0.3) !important;
background: rgba(255,255,255,0.95) !important;
box-shadow: 0 8px 32px rgba(0,0,0,0.1) !important;
}
button {
background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%) !important;
border: none !important;
border-radius: 10px !important;
padding: 12px 30px !important;
font-weight: 600 !important;
font-size: 16px !important;
box-shadow: 0 4px 15px rgba(0,0,0,0.2) !important;
transition: transform 0.2s !important;
}
button:hover {
transform: translateY(-2px) !important;
box-shadow: 0 6px 20px rgba(0,0,0,0.3) !important;
}
.footer {
display: none !important;
}
"""
# Create interface with modern theme
iface = gr.Interface(
fn=classify_text,
inputs=gr.Textbox(
lines=8,
placeholder="✍️ Paste or type text here to analyze...",
label="Enter Text",
elem_id="input-text"
),
outputs=gr.Markdown(label="Analysis Result"),
title="<div id='title'>🔍 AI Text Classifier</div>",
description="<div id='description'>Detect whether text is human-written or AI-generated using advanced NLP</div>",
theme=gr.themes.Soft(
primary_hue="purple",
secondary_hue="pink",
neutral_hue="slate",
font=gr.themes.GoogleFont("Inter")
),
css=custom_css,
flagging_mode="never"
)
iface.launch() |