hindi abusive api added

app.py

@@ -6,6 +6,8 @@ import nltk
 nltk.download('wordnet')
 import numpy as np
 import language_detection
+import requests
+
 print("all imports worked")
 # Load pre-trained model
 model = joblib.load('model_joblib.pkl')
@@ -13,6 +15,12 @@ print("model load ")
 tf = joblib.load('tf_joblib.pkl')
 print("tfidf load ")
 
+def query(payload):
+    API_URL = "https://api-inference.huggingface.co/models/Hate-speech-CNERG/hindi-abusive-MuRIL"
+    headers = {"Authorization": "Bearer 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"}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+    
 # Define function to predict whether sentence is abusive or not
 def predict_abusive_lang(text):
     print("original text ", text)
@@ -34,8 +42,22 @@ def predict_abusive_lang(text):
         else :
             return ["Please write something in the comment box..","No cleaned text"]
     elif lang=='hi':
+        
         print("using hugging face api")
-        return ["Hindi Text abusive part coming soon.....","No cleaned text"]
+        output = query({
+        "inputs": text#"खान चाचा को मेरा सला"
+        })
+        print(output, len(output))
+        # if(len(output))
+        l_0 = float(output[0][0]['score'])
+        l_1 = float(output[0][1]['score'])
+        if  output[0][0]['label']=='LABEL_1' :
+            if l_0>l_1:
+                return ["AB",text]
+    
+        else :
+            return ["NA",text]
+        
     else :
         return ["UN","No cleaned text"]