Upload app (1).py

app (1).py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the sentiment analysis pipeline from Hugging Face Hub
+classifier = pipeline("sentiment-analysis", model="Arvind111/sentiment_newclassifier")
+
+# Define the label mapping from model output to human-readable emotions with emojis
+# Based on previous tests: LABEL_0 -> Neutral, LABEL_1 -> Sad, LABEL_2 -> Happy
+label_mapping = {
+    'LABEL_0': 'Neutral \ud83d\ude10',
+    'LABEL_1': 'Sad \ud83d\ude1e',
+    'LABEL_2': 'Happy \ud83d\ude0a'
+}
+
+def predict_emotion(text):
+    if not text:
+        return "Please enter some text."
+    
+    # Get prediction from the pipeline
+    predictions = classifier(text)
+    
+    if predictions:
+        predicted_label_id = predictions[0]['label']
+        predicted_score = predictions[0]['score']
+        
+        # Map to human-readable label with emoji
+        emotion_label = label_mapping.get(predicted_label_id, "Unknown \ud83e\udd14")
+        
+        return f"Prediction: {emotion_label} (Score: {predicted_score:.4f})"
+    else:
+        return "Could not get prediction."
+
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="text",
+    title="Emotion Prediction with BERT",
+    description="Enter a sentence and the model will predict its primary emotion (Happy, Sad, or Neutral)."
+)
+
+# Launch the Gradio interface
+if __name__ == '__main__':
+    iface.launch(share=False)