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| import gradio as gr | |
| import tensorflow as tf | |
| import text_hammer as th | |
| from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification | |
| tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased") | |
| model = TFDistilBertForSequenceClassification.from_pretrained("Elegbede/Distilbert_FInetuned_For_Text_Classification") | |
| # Define a function to make predictions | |
| def predict(texts): | |
| # Tokenize and preprocess the new text | |
| new_encodings = tokenizer(texts, truncation=True, padding=True, max_length=70, return_tensors='tf') | |
| new_predictions = model(new_encodings) | |
| # Make predictions | |
| new_predictions = model(new_encodings) | |
| new_labels_pred = tf.argmax(new_predictions.logits, axis=1) | |
| new_labels_pred = new_labels_pred.numpy()[0] | |
| labels_list = ["Sadness 😭", "Joy 😂", "Love 😍", "Anger 😠", "Fear 😨", "Surprise 😲"] | |
| emotion = labels_list[new_labels_pred] | |
| return emotion | |
| # Create a Gradio interface | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs="text", | |
| outputs=gr.outputs.Label(num_top_classes = 6), # Corrected output type | |
| examples=[["Tears welled up in her eyes as she gazed at the old family photo."], | |
| ["Laughter filled the room as they reminisced about their adventures."], | |
| ["A handwritten note awaited her on the kitchen table, a reminder of his affection."], | |
| ["Harsh words were exchanged in the heated argument."], | |
| ["The eerie silence of the abandoned building sent shivers down her spine."], | |
| ["She opened the box to find a rare antique hidden inside, a total shock."] | |
| ], | |
| title="Emotion Classification", | |
| description="Predict the emotion associated with a text using my fine-tuned DistilBERT model." | |
| ) | |
| # Launch the interfac | |
| iface.launch() |