Create app.py

app.py

@@ -0,0 +1,52 @@
+import time
+import nltk
+from nltk.sentiment import SentimentIntensityAnalyzer
+import gradio as gr
+from transformers import AutoTokenizer
+from transformers import AutoModelForSequenceClassification
+from scipy.special import softmax
+import nltk
+import matplotlib.pyplot as plt
+nltk.download('vader_lexicon')
+sia = nltk.sentiment.SentimentIntensityAnalyzer()
+
+
+def sentiment_analyzer(text, model_name):
+  positive_score = 0.0
+  negative_score = 0.0
+  if model_name == "Vader":
+      sentiment_score = sia.polarity_scores(text)
+      positive_score = sentiment_score['pos']
+      negative_score = sentiment_score['neg']
+  elif model_name == "RoBERTa":
+      MODEL = f"cardiffnlp/twitter-roberta-base-sentiment"
+      tokenizer = AutoTokenizer.from_pretrained(MODEL)
+      model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+      encoded_text = tokenizer(text, return_tensors='pt')
+      output = model(**encoded_text)
+      scores = output[0][0].detach().numpy()
+      scores = softmax(scores)
+      scores_dict = {
+          'roberta_neg' : scores[0],
+          'roberta_neu' : scores[1],
+          'roberta_pos' : scores[2]
+      }
+      positive_score = float(scores[2])
+      negative_score = float(scores[0])
+
+  # print(f"Output positive score: {positive_score}")
+  # print(f"Output negative score: {negative_score}")
+  return positive_score, negative_score
+    
+sentiment_analyzer("I am a goog human being","RoBERTa")
+
+input_text = gr.inputs.Textbox(lines=15, label="Input Text")
+sentiment_model = gr.inputs.Radio(["Vader", "RoBERTa"], label="Choose Sentiment Analyzer")
+
+outputs = [gr.Slider(minimum=0, maximum=1, step=0.01, label="Positive Sentiment"),
+           gr.Slider(minimum=0, maximum=1, step=0.01, label="Negative Sentiment")]
+
+iface = gr.Interface(fn=sentiment_analyzer, inputs=[input_text, sentiment_model], outputs=outputs)
+
+print(time.time())
+iface.launch(inline=False)