| from tensorflow import keras | |
| import pandas as pd | |
| import numpy as np | |
| # Load model | |
| model = keras.models.load_model("niche_classifier_model.keras") | |
| # Example input (replace this with real preprocessed data) | |
| sample = pd.DataFrame({ | |
| "score": [123], | |
| "num_comments": [45], | |
| "upvote_ratio": [0.92], | |
| "title_length": [84], | |
| "selftext_length": [203], | |
| "contains_question": [1], | |
| "contains_capslock": [0], | |
| "engagement_score": [0.85], | |
| "hour_of_posting": [15], | |
| "num_caps_words": [2], | |
| "subreddit_AskReddit": [1], | |
| "subreddit_relationships": [0], | |
| "subreddit_TIFU": [0], | |
| # add other one-hot subreddit features as needed | |
| }) | |
| pred = model.predict(sample).argmax(axis=1) | |
| print("Predicted class index:", pred) | |