First Commit

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README.md

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+---
+title: AIDetector
+emoji: 🚀
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 5.44.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: Detect AI-generated text (like ZeroGPT) with section highlig
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Coded by Victor M. Writenix.com

app.py

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+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import re
+import pandas as pd
+import gradio as gr
+
+MODEL_NAME = "dejanseo/ai-cop"
+
+# --- Load model ---
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+dtype = torch.bfloat16 if (device.type=="cuda" and torch.cuda.is_bf16_supported()) else torch.float32
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, torch_dtype=dtype)
+model.to(device).eval()
+
+def sent_tokenize(text):
+    return [s for s in re.split(r'(?<=[\.!?])\s+', text.strip()) if s]
+
+# --- Inference function ---
+def classify_text(text):
+    if not text.strip():
+        return "⚠️ Please enter some text.", None, None
+
+    sentences = sent_tokenize(text)
+    if not sentences:
+        return "⚠️ No sentences detected.", None, None
+
+    inputs = tokenizer(
+        sentences,
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+        max_length=model.config.max_position_embeddings
+    ).to(device)
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = F.softmax(logits, dim=-1).cpu()
+        preds = torch.argmax(probs, dim=-1).cpu()
+
+    results = []
+    highlighted_sentences = []
+    for i, s in enumerate(sentences):
+        p = preds[i].item()
+        conf = probs[i, p].item()
+        label = "AI" if p == 0 else "Human"
+
+        results.append([s, label, f"{conf:.2f}"])
+        if label == "AI":
+            highlighted_sentences.append(f"<span style='color:red; font-weight:bold'>{s}</span>")
+        else:
+            highlighted_sentences.append(f"<span style='color:green; font-weight:bold'>{s}</span>")
+
+    # Overall AI likelihood
+    avg = torch.mean(probs, dim=0)
+    model_ai = avg[0].item() * 100
+
+    highlighted_text = " ".join(highlighted_sentences)
+    df = pd.DataFrame(results, columns=["Sentence", "Classification", "Confidence"])
+    return f"⚖️ AI Likelihood: {model_ai:.1f}%", highlighted_text, df
+
+# --- Gradio Interface ---
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 AI Article Detection by Writenix")
+
+    with gr.Row():
+        text_input = gr.Textbox(label="Enter text", lines=10, placeholder="Paste your text here…")
+
+    classify_btn = gr.Button("Detect AI")
+
+    ai_score = gr.Label(label="Overall AI Likelihood")
+    highlighted = gr.HTML()
+    table = gr.Dataframe(headers=["Sentence", "Classification", "Confidence"], wrap=True)
+
+    classify_btn.click(classify_text, inputs=text_input, outputs=[ai_score, highlighted, table])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+streamlit
+torch
+transformers
+nltk
+accelerate