initial commit

.gitignore

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+saved_model/
+*.h5
+*.bin
+*.pt
+*.ckpt
+*.zip
+*.tar
+*.pkl

app.py

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+import tensorflow as tf
+from transformers import (
+    BertTokenizerFast, TFBertForSequenceClassification,
+    RobertaTokenizer, RobertaTokenizerFast, TFRobertaForSequenceClassification,
+    DebertaTokenizer, DebertaTokenizerFast, TFDebertaForSequenceClassification
+)
+import gradio as gr
+from lime.lime_text import LimeTextExplainer
+import numpy as np
+
+true_id2label = {
+    0: 'check_payment_methods', 1: 'check_refund_policy', 2: 'check_cancellation_fee',
+    3: 'check_invoices', 4: 'place_order', 5: 'set_up_shipping_address',
+    6: 'recover_password', 7: 'delivery_period', 8: 'track_refund',
+    9: 'payment_issue', 10: 'contact_customer_service', 11: 'newsletter_subscription',
+    12: 'registration_problems', 13: 'cancel_order', 14: 'review',
+    15: 'contact_human_agent', 16: 'track_order', 17: 'get_invoice',
+    18: 'get_refund', 19: 'change_shipping_address', 20: 'delete_account',
+    21: 'delivery_options', 22: 'create_account', 23: 'change_order',
+    24: 'switch_account', 25: 'complaint', 26: 'edit_account'
+}
+
+class_names = list(true_id2label.values())
+
+models_info = {
+    "BERT": {
+        "path": "vrindaTonk/bert_intent_class",
+        "tokenizer": BertTokenizerFast,
+        "model_class": TFBertForSequenceClassification
+    },
+    "RoBERTa": {
+        "path": "vrindaTonk/roberta_intent_class",
+        "tokenizer": RobertaTokenizer,
+        "model_class": TFRobertaForSequenceClassification
+    },
+    "DeBERTa": {
+        "path": "vrindaTonk/deberta_intent_class",
+        "tokenizer": DebertaTokenizer,
+        "model_class": TFDebertaForSequenceClassification
+    }
+}
+
+# loading models and tokenizers:
+def load_model_and_tokenizer(model_choice):
+    config = models_info[model_choice]
+    tokenizer = config["tokenizer"].from_pretrained(config["path"], local_files_only=True)
+    model = config["model_class"].from_pretrained(config["path"], local_files_only=True)
+    try:
+        id2label = model.config.id2label
+        if "LABEL_0" in id2label.values():
+            id2label = true_id2label
+    except:
+        id2label = true_id2label
+    return tokenizer, model, id2label
+
+explainer = LimeTextExplainer(class_names = class_names)
+
+def predict_and_explainations(model_choice, user_text):
+    tokenizer, model, id2label = load_model_and_tokenizer(model_choice)
+    
+    input = tokenizer(user_text, return_tensors = "tf", truncation = True, padding = True, max_length = 128)
+    logits = model(**input).logits
+    probs = tf.nn.softmax(logits, axis = -1).numpy()[0]
+    predicted_index = tf.argmax(probs).numpy()
+    predictions = {id2label[i] : float(probs[i]) for i in range(len(probs))}
+    
+    def lime_predict(texts):
+        batch = tokenizer(texts, return_tensors = "tf", padding = True, max_length = 128)
+        logits = model(**batch).logits
+        lime_output = tf.nn.softmax(logits, axis = 1).numpy()
+        return lime_output
+    
+    explaination = explainer.explain_instance(user_text, lime_predict, num_features= 10, num_samples = 1000)
+    exp_html = explaination.as_html()
+    fig = explaination.as_pyplot_figure()
+    return predictions, exp_html, fig
+
+# Gradio COde:
+interface = gr.Interface(
+    fn=predict_and_explainations,
+    inputs=[
+        gr.Dropdown(choices=["BERT", "RoBERTa", "DeBERTa"], label="Choose Model"),
+        gr.Textbox(lines=2, label="Customer Query", placeholder="Enter your intent query here...")
+    ],
+    outputs=[
+        gr.Label(num_top_classes=3, label="Predicted Intent Probabilities"),
+        gr.HTML(label="LIME Explanation"),
+        gr.Plot(label = "Lime exp PLOTS")
+    ],
+    title="Intent Classification using Transformers",
+    description="Choose a model, input a customer support query, and get intent predictions with a LIME explanation."
+)
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+gradio
+transformers
+tensorflow
+lime
+matplotlib
+sentencepiece