Addind appilcation file

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import pipeline
+
+def classifier(sentence):
+    classifier = pipeline(
+        "text-classification", 
+        model="AirrStorm/DistilBERT-SST2", 
+        tokenizer="AirrStorm/DistilBERT-SST2",
+        device=0
+    )
+    
+    label_mapping = {"LABEL_0": "negative", "LABEL_1": "positive"}
+    result = classifier(sentence) 
+    predicted_label = label_mapping[result[0]['label']]
+    return predicted_label  # Should print "negative" or "positive"
+
+# Define the Gradio Interface
+demo = gr.Interface(
+    fn=classifier, 
+    inputs=gr.Textbox(
+        lines=4, 
+        placeholder="Type a sentence to classify the sentiment...", 
+        label="Input Text"
+    ), 
+    outputs=gr.Textbox(
+        label="Predicted Sentiment"
+    ),
+    title="Sentiment Analysis", 
+    description="Classify the sentiment of the input text as positive or negative.",
+    theme="hugging-face",  # Optional, you can experiment with other themes like 'huggingface'
+    allow_flagging="never",  # Disable flagging if not needed
+)
+
+# Launch the interface
+demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+transformers
+torch
+SentencePiece
+sacremoses