Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+'''import gradio as gr
 from transformers import TFBertForSequenceClassification, BertTokenizer
 import tensorflow as tf
 
@@ -20,3 +20,28 @@ demo = gr.Interface(fn=classify_sentiment,
                     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()