Update app.py

app.py

@@ -1,32 +1,13 @@
 
-from tqdm import tqdm
-
-from transformers import pipeline, Conversation
+from transformers import pipeline
 import gradio as gr
-# defining classifier
-classifier = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-
-# we can also pass in a list to classifier
-
-# classifier("Wow, it's happening")
-
-text_list = ["This is great", \
-             "Thanks for nothing", \
-             "You've got to work on your face", \
-             "You're beautiful, never change!"]
-
-classifier(text_list)
-# if there are multiple target labels, we can return them all
-classifier = pipeline(task="text-classification", model="SamLowe/roberta-base-go_emotions", top_k=None)
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-message_list = []
-response_list = []
-
-def vanilla_chatbot(message, history):
-    conversation = chatbot(message)
-
-    return conversation[0]['generated_text']
-
-demo_chatbot = gr.ChatInterface(vanilla_chatbot, title="Vanilla Chatbot", description="Enter text to start chatting.")
-
-demo_chatbot.launch(inline=False)
+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',
+                   description = 'Get Sentiment Negative/Positive for the given input')
+iface.launch(inline=False)