Update app.py

app.py

@@ -15,4 +15,69 @@ from sklearn import preprocessing
 import sklearn
 from sklearn.metrics import confusion_matrix
 
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoConfig
+import numpy as np
+from scipy.special import softmax
+import gradio as gr
+
+# Preprocess text (username and link placeholders)
+def preprocess(text):
+    new_text = []
+    for t in text.split(" "):
+        t = '@user' if t.startswith('@') and len(t) > 1 else t
+        t = 'http' if t.startswith('http') else t
+        new_text.append(t)
+    return " ".join(new_text)
+
+# load model
+MODEL = f"cardiffnlp/twitter-roberta-base-sentiment-latest"
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+#model.save_pretrained(MODEL)
+
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+config = AutoConfig.from_pretrained(MODEL)
+
+# create classifier function
+def classify_sentiments(text):
+  text = preprocess(text)
+  encoded_input = tokenizer(text, return_tensors='pt')
+  output = model(**encoded_input)
+  scores = output[0][0].detach().numpy()
+  scores = softmax(scores)
+
+  # Print labels and scores
+  probs = {}
+  ranking = np.argsort(scores)
+  ranking = ranking[::-1]
+
+  for i in range(len(scores)):
+    l = config.id2label[ranking[i]]
+    s = scores[ranking[i]]
+    probs[l] = np.round(float(s), 4)
+  return probs
+
+
+#build the Gradio app
+#Instructuction = "Write an imaginary review about a product or service you might be interested in."
+title="Text Sentiment Analysis"
+description = """Write a Good or Bad review about an imaginary product or service,\
+   see how the machine learning model is able to predict your sentiments"""
+article = """
+            - Click submit button to test sentiment analysis prediction
+            - Click clear button to refresh text
+           """
 
+gr.Interface(,
+            'text',
+            'label',
+            title = title,
+            description = description,
+            #Instruction = Instructuction,
+            article = article,
+            allow_flagging = "never",
+            live = False,
+            examples=["This has to be the best Introductory course in machine learning",
+            "I consider this training an absolute waste of time."]
+             ).launch()