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app.py

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+"""
+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()

requirements.txt

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+flask==3.0.0
+flask-cors==4.0.0
+transformers==4.36.0
+torch==2.1.0
+sentencepiece==0.1.99
+protobuf==4.25.1
+gradio==4.0.0