Spaces:
Sleeping
Sleeping
File size: 2,320 Bytes
67cfdbc f75c14d 1e9cc37 f75c14d |
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 |
import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# ====================== APP CONFIG ======================
st.set_page_config(
page_title="AI Text Detector",
page_icon="🤖",
layout="centered"
)
st.title("🧠 AI Text Detector (DeBERTa-v3-large)")
st.markdown("""
This tool detects whether the given text is **Human-written** or **AI-generated**
using a fine-tuned `microsoft/deberta-v3-large` model.
""")
# ====================== LOAD MODEL ======================
@st.cache_resource
def load_model():
model_name = "kishankachhadiya/debarta-text-classifier" # Replace with your HF model repo
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
return tokenizer, model
tokenizer, model = load_model()
# ====================== TEXT INPUT ======================
user_text = st.text_area(
"Enter text to analyze:",
placeholder="Paste or write any text here...",
height=200
)
if st.button("🔍 Analyze Text", type="primary"):
if not user_text.strip():
st.warning("⚠️ Please enter some text.")
else:
with st.spinner("Analyzing..."):
inputs = tokenizer(user_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0]
confidence, prediction = torch.max(probs, dim=0)
label = "🤖 AI-generated" if prediction.item() == 1 else "🧍 Human-written"
confidence_percent = confidence.item() * 100
st.success(f"**Prediction:** {label}")
st.progress(confidence.item())
st.write(f"**Confidence:** {confidence_percent:.2f}%")
# Detailed Probabilities
st.markdown("### 📊 Detailed Probabilities")
st.write({
"Human (0)": f"{probs[0].item() * 100:.2f}%",
"AI (1)": f"{probs[1].item() * 100:.2f}%"
})
# ====================== FOOTER ======================
st.markdown("---")
st.caption("Built with ❤️ using [Streamlit](https://streamlit.io) and [Hugging Face Transformers](https://huggingface.co/transformers).")
|