feat: hate speech detection

app.py

@@ -1,7 +1,51 @@
+import torch
 import gradio as gr
+import os
+from detoxify import Detoxify
+import pandas as pd
+import json
+import spaces
+import logging
+import datetime
 
-def greet(name):
-    return "Hello " + name + "!!"
+@spaces.GPU
+def classify(query):
+    model = Detoxify("unbiased-small", device="cuda")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+    all_result = []
+    request_type = type(query)
+    try:
+        data = json.loads(query)
+        if type(data) != list:
+            data = [query]
+        else:
+            request_type = type(data)
+    except Exception as e:
+        print(e)
+        data = [query]
+        pass
+
+    for i in range(len(data)):
+        result = {}
+        start_time = datetime.datetime.now()
+        
+        df = pd.DataFrame(model.predict(str(data[i])), index=[0])
+        columns = df.columns
+
+        for i, label in enumerate(columns):
+            result[label] = df[label][0].round(3).astype("float")
+
+        end_time = datetime.datetime.now()
+        elapsed_time = end_time - start_time
+        result["time"] =  str(elapsed_time)
+
+        logging.debug("elapsed predict time: %s", str(elapsed_time))
+        print("elapsed predict time:", str(elapsed_time))
+        
+        all_result.append(result)
+
+    
+    return json.dumps(all_result) if request_type == list else all_result[0]
+
+demo = gr.Interface(fn=classify, inputs=["text"], outputs="text")
 demo.launch()