Update app.py

app.py

@@ -15,10 +15,7 @@ model_id = "gpt2"
 modelgpt2 = GPT2LMHeadModel.from_pretrained(model_id).to(device)
 tokenizergpt2 = GPT2TokenizerFast.from_pretrained(model_id)
 
-def count_words(text):
-    words = text.split()  # Split the text into a list of words
-    return len(words)
-    
+   
 def text_to_sentences(text):
     clean_text = text.replace('\n', ' ')
     return re.split(r'(?<=[^A-Z].[.?]) +(?=[A-Z])', clean_text)
@@ -53,19 +50,20 @@ def calculatePerplexity(text):
     return ppl.item()
     
 def calculatePerplexities(text):
-    if text is None or len(text) == 0:
-        return jsonify({'error': 'No query provided'})
-    if len(text) > 9400:
-        return jsonify({'error': 'Cannot analyze more than 9400 characters!'})
-    if count_words(text) > 1500:
-        return jsonify({'error': 'Cannot analyze more than 1500 words'})
-    
-    sentences = text_to_sentences(text)
-    perplexities = []
-    for sentence in sentences:
-        perplexity = calculatePerplexity(sentence)
-        perplexities.append({"sentence": sentence, "perplexity": perplexity})
-    return jsonify(perplexities)
+    with app.app_context():
+        if text is None or len(text) == 0:
+            return ({'error': 'No query provided'})
+        if len(text) > 9400:
+            return ({'error': 'Cannot analyze more than 9400 characters!'})
+        if len(text.split) > 1500:
+            return ({'error': 'Cannot analyze more than 1500 words'})
+        
+        sentences = text_to_sentences(text)
+        perplexities = []
+        for sentence in sentences:
+            perplexity = calculatePerplexity(sentence)
+            perplexities.append({"sentence": sentence, "perplexity": perplexity})
+        return (perplexities)
 
 def input_api_info():
     return [gr.inputs.Textbox(placeholder="Copy and paste here...")]