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Machine_Human_Classifier
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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()]
    confidence = round(score.item() * 100, 1)
    
    # Format output with icons and styling
    icon = "👤" if label.lower() == "human" else "🤖"
    
    result = f"""
    ### {icon} Classification Result
    
    **Prediction:** {label}-Written Text
    
    **Confidence:** {confidence}%
    
    {'🟦' * int(confidence // 10)} {confidence}%
    """
    
    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;
}
"""

# Example texts
examples = [
    ["The sun set over the horizon, painting the sky in brilliant shades of orange and pink. I sat there, mesmerized by nature's beauty."],
    ["According to recent data analysis, the implementation of artificial intelligence systems has increased operational productivity by approximately 40 percent across various industries."],
    ["Can't believe how amazing that concert was last night! The energy in the crowd was absolutely electric 🎸✨"],
]

# 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>",
    examples=examples,
    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()