app.py

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)