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| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import tensorflow as tf | |
| import pickle | |
| import spacy | |
| from tqdm import tqdm | |
| import gc | |
| import os | |
| # Download the SpaCy model | |
| os.system("python -m spacy download en_core_web_lg") | |
| # Load models | |
| model_1 = tf.keras.models.load_model("model_1.h5") | |
| model_2 = tf.keras.models.load_model("model_2.h5") | |
| model_3 = tf.keras.models.load_model("model_3.h5") | |
| model_4 = tf.keras.models.load_model("model_4.h5") | |
| # Load dictionaries | |
| with open('word_dict.pkl', 'rb') as f: | |
| word_dict = pickle.load(f) | |
| with open('lemma_dict.pkl', 'rb') as f: | |
| lemma_dict = pickle.load(f) | |
| # Load SpaCy NLP model | |
| nlp = spacy.load('en_core_web_lg', disable=['parser', 'ner', 'tagger']) | |
| nlp.vocab.add_flag(lambda s: s.lower() in spacy.lang.en.stop_words.STOP_WORDS, spacy.attrs.IS_STOP) | |
| def preprocess_text(text): | |
| """Preprocess the input text using SpaCy and return word indices.""" | |
| doc = nlp.pipe(text, n_process=1) | |
| word_seq = [] | |
| for token in doc: | |
| if token.pos_ != "PUNCT": | |
| if token.text not in word_dict: | |
| word_dict[token.text] = len(word_dict) + 1 # Increment index | |
| word_seq.append(word_dict[token.text]) | |
| return word_seq | |
| def classify_question(text): | |
| # Preprocess the text | |
| seq = preprocess_text(text) | |
| padded_seq = tf.keras.preprocessing.sequence.pad_sequences([seq], maxlen=55) # Adjust maxlen if needed | |
| BATCH_SIZE = 512 | |
| # Get predictions from each model | |
| pred1 = 0.15 * np.squeeze(model_1.predict(padded_seq, batch_size=BATCH_SIZE, verbose=2)) | |
| pred2 = 0.35 * np.squeeze(model_2.predict(padded_seq, batch_size=BATCH_SIZE, verbose=2)) | |
| pred3 = 0.15 * np.squeeze(model_3.predict(padded_seq, batch_size=BATCH_SIZE, verbose=2)) | |
| pred4 = 0.35 * np.squeeze(model_4.predict(padded_seq, batch_size=BATCH_SIZE, verbose=2)) | |
| # Combine predictions | |
| avg_pred = pred1 + pred2 + pred3 + pred4 | |
| label = "Insincere" if avg_pred > 0.35 else "Sincere" | |
| # Create a list of probabilities for each model | |
| probs = { | |
| "Model 1 Probability": float(pred1), | |
| "Model 2 Probability": float(pred2), | |
| "Model 3 Probability": float(pred3), | |
| "Model 4 Probability": float(pred4), | |
| "Average Probability": float(avg_pred) | |
| } | |
| return label, probs | |
| # Example questions | |
| example_questions = [ | |
| "Is this the best place to get information?", | |
| "I need help with my homework.", | |
| "Why do people ask such stupid questions?", | |
| "Can you tell me the answer to life?" | |
| ] | |
| # Gradio Interface | |
| interface = gr.Interface( | |
| fn=classify_question, | |
| inputs=[ | |
| gr.Textbox(lines=2, placeholder="Enter your question here..."), | |
| # gr.Dropdown(example_questions, label="Select an example question:") | |
| ], | |
| outputs=[ | |
| "text", # Output for label | |
| "json" # Output for probabilities | |
| ], | |
| title="Quora Insincere Questions Classifier", | |
| description="Enter your question to classify it as sincere or insincere. Select an example question from the dropdown." | |
| ) | |
| interface.launch() |