Update app.py

app.py

@@ -7,23 +7,6 @@ from PIL import Image
 st.markdown("Link to the app - [milestone2-app](https://huggingface.co/spaces/aim9061/sentiment-analysis)")
 st.title("Toxic Tweets Sentiment Analysis")
 
-
-words = "Take that, you funking cat-dragon! You smell really bad!"
-text = st.text_area("Insert text for analysis below.", words)
-
-model_list = ["aim9061/fine-tuned-toxic-tweet-dilbert", "distilbert-base-uncased-finetuned-sst-2-english", "bert-base-cased", "openai/clip-vit-base-patch32", "emilyalsentzer/Bio_ClinicalBERT",
-              "sentence-transformers/all-mpnet-base-v2", "facebook/bart-large-cnn", "openai/clip-vit-base-patch16", "speechbrain/spkrec-ecapa-voxceleb", 
-             "albert-base-v2"]
-model = st.selectbox("", model_list)
-sub = st.write("Pick the model to use for analyzing the text!")
-button = st.button("Analyze!")
-pipe = pipeline("text-classification")
-if(button):
-    if model == "aim9061/fine-tuned-toxic-tweet-dilbert":
-      get_cats(text)
-    pipe = pipeline("text-classification", model)
-    results = pipe(text)
-    st.write(results)
 def create_dict(prob, text):
   sorted_indices = np.argsort(prob)[-2:]
   info = {"text": text,
@@ -42,6 +25,24 @@ def get_cats(text):
   
   data = create_dict(prob, text)
   st.table(data)
+
+words = "Take that, you funking cat-dragon! You smell really bad!"
+text = st.text_area("Insert text for analysis below.", words)
+
+model_list = ["aim9061/fine-tuned-toxic-tweet-dilbert", "distilbert-base-uncased-finetuned-sst-2-english", "bert-base-cased", "openai/clip-vit-base-patch32", "emilyalsentzer/Bio_ClinicalBERT",
+              "sentence-transformers/all-mpnet-base-v2", "facebook/bart-large-cnn", "openai/clip-vit-base-patch16", "speechbrain/spkrec-ecapa-voxceleb", 
+             "albert-base-v2"]
+model = st.selectbox("", model_list)
+sub = st.write("Pick the model to use for analyzing the text!")
+button = st.button("Analyze!")
+pipe = pipeline("text-classification")
+if(button):
+    if model == "aim9061/fine-tuned-toxic-tweet-dilbert":
+      get_cats(text)
+    pipe = pipeline("text-classification", model)
+    results = pipe(text)
+    st.write(results)
+
   
 #TODO: DOCUMENT CODE