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
 
@@ -20,67 +20,5 @@ 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()
-'''
-import gradio as gr
-from transformers import TFBertForSequenceClassification, BertTokenizer
-import tensorflow as tf
 
-# Load model and tokenizer from Hugging Face Hub
-model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
-tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
-
-def classify_sentiment(text):
-    text = text.lower().strip()
-    inputs = tokenizer(text, return_tensors="tf", padding=True, truncation=True)
-    outputs = model(inputs, training=False)
-    logits = outputs.logits
-    label_id = int(tf.argmax(logits, axis=1).numpy()[0])
-    
-    # Handle label mapping correctly
-    raw_labels = model.config.id2label
-    if isinstance(list(raw_labels.keys())[0], str):
-        label = raw_labels.get(str(label_id), "Unknown")
-    else:
-        label = raw_labels.get(label_id, "Unknown")
-    
-    print(f"Text: {text} | Label ID: {label_id} | Label: {label} | Logits: {logits.numpy()}")
-    return label
-
-# Define the Gradio interface
-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"
-)
-
-# Launch the app
-demo.launch()