Update app.py

app.py

@@ -1,23 +1,14 @@
-import os
-import gradio as gr
-from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
-
-HF_TOKEN = os.environ.get('HF_TOKEN')
-
-model_checkpoint = "besijar/dspa_review_classification"
-tokeniser = AutoTokenizer.from_pretrained(model_checkpoint, use_auth_token=HF_TOKEN)
-model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint, use_auth_token=HF_TOKEN)
-
-example_review = "Tully's House Blend is the perfect K-Cup for me.  Sure, I occasionally enjoy the special flavors.....Mocha, Italian roast, French vanilla, but my favorite 'go-to'coffee is House Blend. Wakes me up in the morning with it's coffee house full hearty taste."
-
-def review_classify(review):
-    review = tokeniser.encode(review)
-    review = model.predict([review])
-    return int(review.logits.argmax())
-
-iface = gr.Interface(review_classify,
-                        title="Review Classification using DistilRoBERTa",
-                        inputs=[gr.Text(label="Review")],
-                        outputs=[gr.Number(label="Rating", precision=0)],
-                        examples=[example_review])
+import gradio as gr 
+from transformers import pipeline
+sentiment = pipeline('sentiment-analysis')
+def get_sentiment(input_text):
+  return sentiment (input_text)
+iface = gr.Interface(fn = get_sentiment,
+                     inputs = 'text',
+                     outputs = ['text'],
+                     title = 'Sentiment Analysis',
+                     examples = ['The movie was very bad', 'Every day is a new opportunity.'],
+                     article = 'This project is for software engineering with team members Aditya Jadhav, Sujal Kuthe, Sujal Wakalkar, and Adesh Ingle. We developed a web application for sentiment analysis that takes text data as input and classifies whether it is positive or negative.',
+                     thumbnail = '/content/sentiment-analysis.png',
+                     theme = gr.themes.Soft())
 iface.launch()