Spaces:
Sleeping
Sleeping
| """ | |
| AI Detection & Humanization API - Hugging Face Spaces Version | |
| This is a simplified Gradio interface for Hugging Face Spaces deployment | |
| """ | |
| import gradio as gr | |
| from transformers import ( | |
| AutoTokenizer, | |
| AutoModelForSequenceClassification, | |
| PegasusTokenizer, | |
| PegasusForConditionalGeneration | |
| ) | |
| import torch | |
| import json | |
| import os | |
| # Global variables for models | |
| ai_detector_model = None | |
| ai_detector_tokenizer = None | |
| humanizer_model = None | |
| humanizer_tokenizer = None | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| def load_models(): | |
| """Load both AI detection and humanization models""" | |
| global ai_detector_model, ai_detector_tokenizer, humanizer_model, humanizer_tokenizer | |
| print("Loading AI detection model...") | |
| ai_detector_tokenizer = AutoTokenizer.from_pretrained("Hello-SimpleAI/chatgpt-detector-roberta") | |
| ai_detector_model = AutoModelForSequenceClassification.from_pretrained("Hello-SimpleAI/chatgpt-detector-roberta") | |
| ai_detector_model.to(device) | |
| ai_detector_model.eval() | |
| print("AI detection model loaded!") | |
| print("Loading humanization model...") | |
| humanizer_tokenizer = PegasusTokenizer.from_pretrained("tuner007/pegasus_paraphrase") | |
| humanizer_model = PegasusForConditionalGeneration.from_pretrained("tuner007/pegasus_paraphrase") | |
| humanizer_model.to(device) | |
| humanizer_model.eval() | |
| print("Humanization model loaded!") | |
| def detect_ai(text): | |
| """Detect if text is AI-generated""" | |
| if not text or len(text.strip()) == 0: | |
| return "Please enter some text to analyze." | |
| try: | |
| inputs = ai_detector_tokenizer( | |
| text, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512, | |
| padding=True | |
| ).to(device) | |
| with torch.no_grad(): | |
| outputs = ai_detector_model(**inputs) | |
| predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) | |
| ai_prob = predictions[0][0].item() * 100 | |
| human_prob = predictions[0][1].item() * 100 | |
| if ai_prob > human_prob: | |
| result = f"""🤖 **AI-Generated Text Detected** | |
| **Confidence:** {ai_prob:.1f}% | |
| | Metric | Value | | |
| |--------|-------| | |
| | AI Probability | {ai_prob:.1f}% | | |
| | Human Probability | {human_prob:.1f}% | | |
| | Label | AI-Generated | | |
| """ | |
| else: | |
| result = f"""✅ **Human-Written Text Detected** | |
| **Confidence:** {human_prob:.1f}% | |
| | Metric | Value | | |
| |--------|-------| | |
| | AI Probability | {ai_prob:.1f}% | | |
| | Human Probability | {human_prob:.1f}% | | |
| | Label | Human-Written | | |
| """ | |
| return result | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| def humanize_text(text): | |
| """Humanize AI-generated text""" | |
| if not text or len(text.strip()) == 0: | |
| return "Please enter some text to humanize." | |
| try: | |
| inputs = humanizer_tokenizer( | |
| text, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512, | |
| padding=True | |
| ).to(device) | |
| with torch.no_grad(): | |
| outputs = humanizer_model.generate( | |
| **inputs, | |
| max_length=512, | |
| num_beams=4, | |
| early_stopping=True, | |
| length_penalty=1.0 | |
| ) | |
| humanized = humanizer_tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return humanized | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| def process_combined(text, auto_humanize): | |
| """Combined: Detect and optionally humanize""" | |
| if not text or len(text.strip()) == 0: | |
| return "Please enter some text.", "" | |
| # First detect | |
| detection = detect_ai(text) | |
| # Check if humanization is needed | |
| humanized = "" | |
| if auto_humanize: | |
| # Parse AI probability from detection result | |
| if "AI-Generated" in detection: | |
| humanized = humanize_text(text) | |
| else: | |
| humanized = "No humanization needed - text appears to be human-written." | |
| return detection, humanized | |
| # Load models at startup | |
| print("Initializing models (this may take a few minutes)...") | |
| load_models() | |
| print("Models loaded successfully!") | |
| # Create Gradio interface | |
| with gr.Blocks( | |
| title="AI Detection & Humanization API", | |
| theme=gr.themes.Soft() | |
| ) as demo: | |
| gr.Markdown(""" | |
| # 🤖 AI Detection & Humanization API | |
| Detect AI-generated text and humanize it to sound more natural. | |
| **Your API Key:** `sk-demo-key-12345678` | |
| --- | |
| """) | |
| with gr.Tab("🔍 AI Detection"): | |
| gr.Markdown("### Detect if text is AI-generated") | |
| with gr.Row(): | |
| with gr.Column(): | |
| detect_input = gr.Textbox( | |
| label="Enter text to analyze", | |
| placeholder="Paste your text here...", | |
| lines=6 | |
| ) | |
| detect_btn = gr.Button("Detect AI", variant="primary", size="lg") | |
| with gr.Column(): | |
| detect_output = gr.Markdown(label="Detection Result") | |
| detect_btn.click(detect_ai, inputs=detect_input, outputs=detect_output) | |
| gr.Examples( | |
| examples=[ | |
| ["Artificial intelligence has revolutionized numerous industries by providing innovative solutions to complex problems. Machine learning algorithms can analyze vast amounts of data to identify patterns."], | |
| ["Hey! I just grabbed coffee with my friend yesterday. The weather was amazing and we had such a great time chatting!"], | |
| ], | |
| inputs=detect_input | |
| ) | |
| with gr.Tab("✍️ Humanization"): | |
| gr.Markdown("### Make AI text sound more human") | |
| with gr.Row(): | |
| with gr.Column(): | |
| humanize_input = gr.Textbox( | |
| label="Enter AI-generated text to humanize", | |
| placeholder="Paste AI-generated text here...", | |
| lines=6 | |
| ) | |
| humanize_btn = gr.Button("Humanize Text", variant="primary", size="lg") | |
| with gr.Column(): | |
| humanize_output = gr.Textbox( | |
| label="Humanized Text", | |
| lines=6 | |
| ) | |
| humanize_btn.click(humanize_text, inputs=humanize_input, outputs=humanize_output) | |
| gr.Examples( | |
| examples=[ | |
| ["Artificial intelligence has revolutionized numerous industries by providing innovative solutions to complex problems."], | |
| ["The implementation of machine learning algorithms facilitates the optimization of business processes."], | |
| ], | |
| inputs=humanize_input | |
| ) | |
| with gr.Tab("⚡ Combined Processing"): | |
| gr.Markdown("### Detect AI and humanize in one step") | |
| with gr.Row(): | |
| with gr.Column(): | |
| combined_input = gr.Textbox( | |
| label="Enter text to process", | |
| placeholder="Paste your text here...", | |
| lines=6 | |
| ) | |
| auto_humanize = gr.Checkbox( | |
| label="Auto-humanize if AI is detected", | |
| value=True | |
| ) | |
| combined_btn = gr.Button("Process Text", variant="primary", size="lg") | |
| with gr.Column(): | |
| combined_detection = gr.Markdown(label="Detection Result") | |
| combined_humanized = gr.Textbox(label="Humanized Text", lines=4) | |
| combined_btn.click( | |
| process_combined, | |
| inputs=[combined_input, auto_humanize], | |
| outputs=[combined_detection, combined_humanized] | |
| ) | |
| with gr.Tab("📚 API Documentation"): | |
| gr.Markdown(""" | |
| ## API Endpoints | |
| This Space also provides REST API endpoints that you can call programmatically. | |
| ### Base URL | |
| ``` | |
| https://neptests-ai-detection-api.hf.space | |
| ``` | |
| ### 1. Detect AI Text | |
| ```python | |
| import requests | |
| response = requests.post( | |
| "https://neptests-ai-detection-api.hf.space/api/predict", | |
| json={"data": ["Your text here"]} | |
| ) | |
| print(response.json()) | |
| ``` | |
| ### 2. Humanize Text | |
| ```python | |
| response = requests.post( | |
| "https://neptests-ai-detection-api.hf.space/api/predict_1", | |
| json={"data": ["AI text to humanize"]} | |
| ) | |
| print(response.json()) | |
| ``` | |
| ### Your API Key | |
| ``` | |
| sk-demo-key-12345678 | |
| ``` | |
| --- | |
| ## Features | |
| - ✅ **AI Detection** - Detect if text is AI-generated | |
| - ✅ **Text Humanization** - Convert AI text to human-like | |
| - ✅ **Combined Processing** - Detect and humanize together | |
| - ✅ **FREE to use** - No payment required | |
| --- | |
| Built with ❤️ using Gradio and Hugging Face Transformers | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch() | |