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| import sys | |
| import os | |
| sys.path.append(os.path.join(os.path.dirname(__file__), "src")) | |
| from flask import Flask, request, jsonify | |
| import gradio as gr | |
| from model import ToxicCommentDetector | |
| app = Flask(__name__) | |
| detector = ToxicCommentDetector() | |
| detector.load_models() | |
| def predict(): | |
| data = request.json | |
| text = data.get('text', '') | |
| model_name = data.get('model_name', 'DistilBERT') | |
| if not text: | |
| return jsonify({"error": "No text provided"}), 400 | |
| try: | |
| results = detector.predict(text, model_name) | |
| return jsonify(results) | |
| except Exception as e: | |
| return jsonify({"error": str(e)}), 500 | |
| def create_gradio_interface(detector): | |
| def predict_toxicity(text, model_name): | |
| if not text.strip(): | |
| return "Please enter some text to analyze." | |
| try: | |
| results = detector.predict(text, model_name) | |
| output = f"🔍 **Analysis Results using {model_name}:**\n\n" | |
| for label, score in results.items(): | |
| emoji = "🚨" if score > 0.5 else "✅" | |
| output += f"{emoji} **{label.replace('_', ' ').title()}**: {score:.3f} ({score*100:.1f}%)\n" | |
| return output | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| with gr.Blocks(title="🛡️ Toxic Comment Detector", theme=gr.themes.Soft()) as interface: | |
| gr.Markdown(""" | |
| # 🛡️ Toxic Comment Detector | |
| This app uses three different pre-trained models to detect toxicity in comments. | |
| Enter your text below and choose a model to get predictions, or compare all models at once! | |
| """) | |
| with gr.Tab("Single Model Prediction"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| text_input = gr.Textbox(label="Enter comment to analyze", placeholder="Type your comment here...", lines=3) | |
| model_dropdown = gr.Dropdown(choices=list(detector.models.keys()), label="Select Model", value=list(detector.models.keys())[0]) | |
| predict_btn = gr.Button("🔍 Analyze Toxicity", variant="primary") | |
| with gr.Column(): | |
| single_output = gr.Markdown(label="Results") | |
| predict_btn.click(predict_toxicity, inputs=[text_input, model_dropdown], outputs=single_output) | |
| return interface | |
| if __name__ == "__main__": | |
| interface = create_gradio_interface(detector) | |
| interface.launch() |