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'''import gradio as gr
from transformers import TFBertForSequenceClassification, BertTokenizer
import tensorflow as tf
# Load model and tokenizer from your HF model repo
model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
def classify_sentiment(text):
inputs = tokenizer(text, return_tensors="tf", padding=True, truncation=True)
predictions = model(inputs).logits
label = tf.argmax(predictions, axis=1).numpy()[0]
labels = {0: "Negative", 1: "Neutral", 2: "Positive"}
return labels[label]
demo = gr.Interface(fn=classify_sentiment,
inputs=gr.Textbox(placeholder="Enter a tweet..."),
outputs="text",
title="Tweet Sentiment Classifier",
description="Multilingual BERT-based Sentiment Analysis")
demo.launch()
'''
import gradio as gr
from transformers import TFBertForSequenceClassification, BertTokenizer
import tensorflow as tf
# Load model and tokenizer from Hugging Face
model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
# Manually define the correct mapping
LABELS = {
0: "Neutral",
1: "Positive",
2: "Negative"
}
def classify_sentiment(text):
inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
outputs = model(inputs)
probs = tf.nn.softmax(outputs.logits, axis=1)
pred_label = tf.argmax(probs, axis=1).numpy()[0]
confidence = float(tf.reduce_max(probs).numpy())
return f"Prediction: {LABELS[pred_label]} (Confidence: {confidence:.2f})"
demo = gr.Interface(
fn=classify_sentiment,
inputs=gr.Textbox(placeholder="Type your tweet here..."),
outputs="text",
title="Sentiment Analysis on Tweets",
description="Multilingual BERT model fine-tuned for sentiment classification. Labels: Positive, Neutral, Negative."
)
demo.launch()
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