Create app.py

app.py

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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-classifier")
+tokenizer = BertTokenizer.from_pretrained("Shrish191/sentiment-classifier")
+
+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()