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app.py

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+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import gradio as gr
+
+def text_sentiments(text):
+
+  model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+  
+  model = AutoModelForSequenceClassification.from_pretrained(model_name)
+  tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+  classifier = pipeline(task= "sentiment-analysis", model = model, tokenizer = tokenizer)
+
+  result = classifier(text)
+
+  label = result[0]["label"]
+  score = result[0]["score"] * 100
+
+  return f"Sentiment is : {label} and Confidence is : {score: 0.2f} %"
+
+
+gr.Interface(fn = text_sentiments, 
+             inputs = gr.inputs.Textbox(label = "Input Text"),
+             outputs = gr.outputs.Textbox(),
+             title = "Sentiment Classification with Bert",
+             ).launch()
+
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requirements.txt

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+transformers
+gradio