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| import gradio as gr | |
| import torch | |
| from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification | |
| from datetime import datetime | |
| import csv | |
| import os | |
| # Load model and tokenizer | |
| model = DistilBertForSequenceClassification.from_pretrained("debojit01/course-review-sentiment") | |
| tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased") | |
| labels = ['negative', 'neutral', 'positive'] | |
| # Setup log files | |
| log_path = "logs.csv" | |
| corrections_path = "corrections.csv" | |
| for path, headers in [(log_path, ["timestamp", "input_text", "predicted_label"]), | |
| (corrections_path, ["timestamp", "input_text", "predicted_label", "user_correction"])]: | |
| if not os.path.exists(path): | |
| with open(path, mode='w', newline='') as file: | |
| writer = csv.writer(file) | |
| writer.writerow(headers) | |
| def classify_review(text): | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0] | |
| prob_dict = {label: float(prob) for label, prob in zip(labels, probs)} | |
| predicted_label = labels[torch.argmax(probs).item()] | |
| # Logging | |
| with open(log_path, mode='a', newline='') as file: | |
| writer = csv.writer(file) | |
| writer.writerow([datetime.now().isoformat(), text, predicted_label]) | |
| return prob_dict, text, predicted_label | |
| def save_correction(text, predicted_label, user_correction): | |
| if user_correction != predicted_label: | |
| with open(corrections_path, mode='a', newline='') as file: | |
| writer = csv.writer(file) | |
| writer.writerow([datetime.now().isoformat(), text, predicted_label, user_correction]) | |
| return f"✅ Thanks! Correction recorded: {user_correction}" | |
| # Gradio UI | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 📘 Course Review Sentiment Classifier") | |
| gr.Markdown("Enter a course review and get the sentiment prediction. You can correct the result if needed.") | |
| input_text = gr.Textbox(lines=4, placeholder="Enter course review here...") | |
| output_label = gr.Label(num_top_classes=3, label="Predicted Sentiment") | |
| predict_btn = gr.Button("Classify") | |
| with gr.Row(visible=False) as correction_row: | |
| gr.Markdown("### ❓ Is the prediction wrong?") | |
| correction_dropdown = gr.Dropdown(choices=labels, label="Correct Sentiment") | |
| submit_btn = gr.Button("Submit Correction") | |
| correction_status = gr.Textbox(interactive=False) | |
| hidden_text = gr.Textbox(visible=False) | |
| hidden_pred = gr.Textbox(visible=False) | |
| def show_correction_ui(_, text, pred): | |
| return gr.update(visible=True), text, pred | |
| predict_btn.click(classify_review, inputs=input_text, outputs=[output_label, hidden_text, hidden_pred]) \ | |
| .then(show_correction_ui, inputs=[output_label, hidden_text, hidden_pred], | |
| outputs=[correction_row, hidden_text, hidden_pred]) | |
| submit_btn.click(save_correction, inputs=[hidden_text, hidden_pred, correction_dropdown], outputs=correction_status) | |
| if __name__ == "__main__": | |
| demo.launch() |