Update app.py

app.py

@@ -39,7 +39,7 @@ user_input="""Brooo, you won’t believe what happened today 😭 So I’m just
 
 
 @tool
-def detect_text(text):
+def detect_text(texts):
     """ Process AI text detection using fakespot-ai/roberta-base-ai-text-detection-v1 model.
     It returns its result in a List[Dict] form.
     e.g. [{'label': 'AI', 'score': 0.9998624324798584}]
@@ -49,11 +49,31 @@ def detect_text(text):
         model=TEXT_DETECTOR_MODEL_ID
     )
 
-    cleaned_text = classifier(clean_text(text))
-    label_result = cleaned_text[0]["label"]
-    score_result = cleaned_text[0]["score"]
+    ### Split text into max 300 words into multiple batches in case the text contains more than 350 words
+    ### due to the max token limit of the detection model
+    num_words = 300
+    batched_words = []
+    words: List = texts.split()
 
-    return label_result, score_result
+    if len(texts.split()) >= num_words:
+        for i in range(0, len(words), num_words):
+            chunk = " ".join(words[i: i + num_words])
+            batched_words.append(chunk)
+
+            cleaned_text = classifier([clean_text(t) for t in batched_words])
+            label_result = cleaned_text[0]["label"]
+            score_result = cleaned_text[0]["score"]
+
+        return label_result, score_result
+    else:
+        cleaned_text = classifier(clean_text(texts))
+
+        ### Works with a batch of texts
+        label_result = cleaned_text[0]["label"]
+        score_result = cleaned_text[0]["score"]
+
+        return label_result, score_result
+        
 
 def generate_dynamic_query(text, label, score):