Collect input from user and display related toxicity information

app.py

@@ -19,7 +19,49 @@ vectorizer = TextVectorization(max_tokens=MAX_FEATURES,
 
 vectorizer.adapt(X.values)
 
-input_str = vectorizer('I hate you.')
+st.title('Toxicity Classifier')
+
+'''
+st.header('Paste the link to a tweet here:')
+text = st.text_area('The toxicity of the tweet will be evaluated.',
+                   value = "https://twitter.com/lowtiiergod/status/1592437600216035328")
+'''
+
+st.header('Write a message here:')
+text = st.text_area('The toxicity of the message will be evaluated.',
+                   value = "I hate your guts and you should die alone.")
+
+input_str = vectorizer(text)
 res = model.predict(np.expand_dims(input_str,0))
 classification = res[0].tolist()
-st.write(classification)
+
+toxicity = classification[0]
+toxicity_severe = classification[1]
+obscene = classification[2]
+threat = classification[3]
+insult = classification[4]
+identity_hate = classification[5]
+
+highest_class = "Severe toxicity"
+highest_class_rating = toxicity_severe
+if(obscene > highest_class_rating):
+    highest_class = "Obscenity"
+    highest_class_rating = obscene
+if(threat > highest_class_rating):
+    highest_class = "Threat"
+    highest_class_rating = threat
+if(insult > highest_class_rating):
+    highest_class = "Insult"
+    highest_class_rating = insult
+if(identity_hate > highest_class_rating):
+    highest_class = "Identity hate"
+    highest_class_rating = identity_hate
+
+st.write("---")
+st.write("Toxicity rating:")
+st.write(toxicity)
+st.write("---")
+st.write("Highest Toxicity Class:")
+st.write(highest_class)
+st.write("Rating:")
+st.write(highest_class_rating)