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| import gradio as gr | |
| import torch | |
| from fastai.text.all import * | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| # Load the trained model | |
| def load_model(): | |
| try: | |
| learn = load_learner('js_concept_tagger_simple_numbers.pkl') | |
| return learn | |
| except Exception as e: | |
| print(f"Error loading model: {e}") | |
| return None | |
| # Initialize model | |
| model = load_model() | |
| def predict_js_concept(code_snippet): | |
| """Predict JavaScript concept for given code snippet""" | |
| if model is None: | |
| return "Error: Model not loaded", 0.0, "Model loading failed" | |
| try: | |
| # Make prediction | |
| pred, pred_idx, probs = model.predict(code_snippet) | |
| # Get the predicted concept and confidence | |
| predicted_concept = str(pred) | |
| confidence = float(probs.max()) | |
| # Get all predictions with probabilities | |
| concept_names = model.dls.vocab[1] # label vocabulary | |
| all_predictions = [] | |
| for i, prob in enumerate(probs): | |
| if prob > 0.1: # Show predictions above 10% | |
| all_predictions.append(f"{concept_names[i]}: {float(prob):.3f}") | |
| all_predictions_str = "\n".join(all_predictions) | |
| return predicted_concept, confidence, all_predictions_str | |
| except Exception as e: | |
| return f"Error: {str(e)}", 0.0, "Prediction failed" | |
| # Create Gradio interface | |
| def create_interface(): | |
| # Example code snippets for users to try | |
| examples = [ | |
| ["const age = 25;"], | |
| ["const name = 'John Doe';"], | |
| ["const isActive = true;"], | |
| ["const items = [1, 2, 3];"], | |
| ["function greet() { return 'Hello'; }"], | |
| ["if (x > 0) { console.log('positive'); }"], | |
| ["for (let i = 0; i < 10; i++) { }"], | |
| ["arr.push(newItem);"], | |
| ["const sum = 10 + 20;"], | |
| ["let message = 'Welcome to our app';"] | |
| ] | |
| interface = gr.Interface( | |
| fn=predict_js_concept, | |
| inputs=[ | |
| gr.Textbox( | |
| label="JavaScript Code Snippet", | |
| placeholder="Enter your JavaScript code here...", | |
| lines=3, | |
| max_lines=10 | |
| ) | |
| ], | |
| outputs=[ | |
| gr.Textbox(label="Predicted Concept", interactive=False), | |
| gr.Number(label="Confidence Score", precision=3), | |
| gr.Textbox(label="All Predictions", lines=6, interactive=False) | |
| ], | |
| title="🔍 JavaScript Concept Classifier", | |
| description=""" | |
| This AI model classifies JavaScript code snippets into different programming concepts: | |
| **Supported Concepts:** | |
| - 🔢 **Numbers**: Numeric values and assignments | |
| - 📝 **Strings**: Text and string literals | |
| - ✅ **Booleans**: True/false values | |
| - 📋 **Arrays**: Array operations and methods | |
| - ⚡ **Functions**: Function definitions and calls | |
| - 🔄 **Control Flow**: If statements, loops, switches | |
| **How to use:** Simply paste your JavaScript code in the text box below and click Submit! | |
| """, | |
| examples=examples, | |
| theme=gr.themes.Soft(), | |
| allow_flagging="never" | |
| ) | |
| return interface | |
| # Launch the interface | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch() | |