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| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| from transformers import TFAutoModelForSequenceClassification, DistilBertTokenizer | |
| from huggingface_hub import hf_hub_download | |
| # Define the repository name and model ID | |
| repository_name = "MariamKili/sentiment_bert_model" | |
| model_id = "tf_model" | |
| # Load the tokenizer | |
| tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased") | |
| # Load the model directly from Hugging Face Hub | |
| model = TFAutoModelForSequenceClassification.from_pretrained(repository_name) | |
| # Your prediction function would remain the same | |
| def predict_sentiment(text): | |
| # Tokenize and encode the input text | |
| encoded_input = tokenizer.encode_plus( | |
| text, | |
| add_special_tokens=True, | |
| max_length=512, | |
| padding="max_length", | |
| return_attention_mask=True, | |
| truncation=True, | |
| return_tensors="tf" | |
| ) | |
| # Make predictions | |
| output = model(encoded_input) | |
| probabilities = tf.nn.softmax(output.logits, axis=1).numpy()[0] | |
| predicted_label = np.argmax(probabilities) | |
| confidence_score = probabilities[predicted_label] | |
| # Decode the predicted label | |
| label = "positive" if predicted_label == 1 else "negative" | |
| return label, confidence_score | |
| # Create the Gradio interface | |
| text_input = gr.components.Textbox(lines=5, label="Enter your text here") | |
| output_text = gr.components.Textbox(label="Predicted Sentiment") | |
| # Define the Gradio interface | |
| iface=gr.Interface(fn=predict_sentiment, inputs=text_input, outputs=output_text, title="Sentiment Analysis Application System") | |
| # Launch the Gradio app | |
| iface.launch(share=True) |