Update app.py

app.py

@@ -44,9 +44,11 @@ def analyze_output(vtext: str):
     pipe = pipeline("text-classification", model="Titeiiko/OTIS-Official-Spam-Model")
     x = pipe(vtext)[0]
     if x["label"] == "LABEL_0":
-        spammy = {"type":"Not Spam", "probability":x["score"]}
+        spammy = "Not Spammy"
+        #{"type":"Not Spam", "probability":x["score"]}
     else:
-        spammy = {"type":"Spam", "probability":x["score"]}
+        spammy = "Spammy"
+        #{"type":"Spam", "probability":x["score"]}
         
     return "spammy"
 ### Spammy or not
@@ -62,8 +64,7 @@ def measure_readability(message,history):
         text = re.sub(CLEANR,'', cleantext)
     else:
         text = message
-
-    vtext = text
+    
     vline1 = "==== Content Info ==== " + os.linesep
     vline2 = "Character Count "+str(textstat.char_count(text, ignore_spaces=True)) + os.linesep
     vline3 = "Lexicon Count "+str(textstat.lexicon_count(text, removepunct=True)) + os.linesep
@@ -85,11 +86,11 @@ def measure_readability(message,history):
     pipe = pipeline("text-classification", model="Titeiiko/OTIS-Official-Spam-Model")
     x = pipe(text)[0]
     if x["label"] == "LABEL_0":
-        spammy = {"type":"Not Spam", "probability":x["score"]}
+        spammy = "Not Spammy"
+        #{"type":"Not Spam", "probability":x["score"]}
     else:
-        spammy = {"type":"Spam", "probability":x["score"]}
-
-    vspam = str(spammy)
+        spammy = "Spammy"
+        #{"type":"Spam", "probability":x["score"]} 
     
     answer = vline1+vline2+vline3+vline4+vline5+vline6+vline7+vline8+vline9+vline10+vline11+vline12+vline14+spammy
     #answer = "Flesch Reading Ease (90-100 = Easy to read, 0-29 = Very confusing to read) is "+str(textstat.flesch_reading_ease(text))