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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 your HF model repo
model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
def classify_sentiment(text):
text = text.lower().strip() # Normalize input
inputs = tokenizer(text, return_tensors="tf", padding=True, truncation=True)
predictions = model(inputs).logits
label = tf.argmax(predictions, axis=1).numpy()[0]
labels = model.config.id2label # Use mapping from config.json
print(f"Text: {text} | Prediction: {label} | Logits: {predictions.numpy()}") # Debug
return labels[str(label)] # Convert to string key
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()
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