Update app.py

app.py

@@ -1,3 +1,5 @@
+
+
 # Import a module
 from transformers import AutoModelForSequenceClassification
 from transformers import TFAutoModelForSequenceClassification
@@ -39,8 +41,11 @@ config.id2label = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
 config.label2id = {"NEGATIVE": 0, "NEUTRAL": 1, "POSITIVE": 2}
 
 # creating a function used for gradio app
-def sentiment_analysis(text):
+# Creating dictionary
+dictionary = {}
 
+def sentiment_analysis(text):
+    
     # Create a new dictionary
     scores = {}
     
@@ -53,14 +58,22 @@ def sentiment_analysis(text):
     scores = output[0][0].detach().numpy()
     scores = softmax(scores)
     
-    labels = {0: "NEGAITVE", 1: "NEUTRAL", 2: "POSITIVE"}
-    scores = {labels[i]: float(s) for i , s in enumerate(scores) }
+    # Convert the numpy array into a list
+    scores = scores.tolist()
+    ranking = np.argsort(scores)
+    ranking = ranking[::-1]
+    
+    for i in range(len(scores)):
+        l = config.id2label[ranking[i]]
+        s = scores[ranking[i]]
+       
+        # Convert the numpy float32 object into a float
+        scores[l] = float(s)
     
     # Return the dictionary as the response content
     return scores
 
 
-
 # Create your interface
 demo = gr.Interface(
     fn=sentiment_analysis,