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
 
@@ -20,5 +20,38 @@ 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 Hugging Face
+model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
+tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
+
+# Manually define the correct mapping
+LABELS = {
+    0: "Negative",
+    1: "Neutral",
+    2: "Positive"
+}
+
+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()