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| import gradio as gr | |
| import json | |
| from bertopic import BERTopic | |
| import os | |
| # Load BERTopic model | |
| model_path = os.path.join("topic_model") # folder where you uploaded the model | |
| topic_model = BERTopic.load(model_path) | |
| # Load topic mapping | |
| mapping_path = os.path.join("topic_model", "topic_final_mapping.json") | |
| with open(mapping_path, "r", encoding="utf-8") as f: | |
| topic_mapping = json.load(f) | |
| # Prediction function | |
| def predict_comment(text): | |
| if not text.strip(): | |
| return "No input", "Other" | |
| topics, _ = topic_model.transform([text]) | |
| topic_id = str(topics[0]) | |
| final_topic = topic_mapping.get(topic_id, "Other") | |
| return topic_id, final_topic | |
| # Build Gradio interface | |
| iface = gr.Interface( | |
| fn=predict_comment, | |
| inputs=gr.Textbox(lines=3, placeholder="Type or paste a YouTube comment..."), | |
| outputs=["text", "text"], | |
| title="AFCON Comments Topic Classifier", | |
| description="Type or paste a YouTube comment and get its predicted topic_final." | |
| ) | |
| # Launch interface | |
| iface.launch() | |