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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) |