Update app.py

app.py

@@ -6,6 +6,9 @@ from flask_limiter.util import get_remote_address
 from routes.helpers import checkSignature, configFile
 from routes import *
 
+from random import randint
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
 #initing
 app = Flask(__name__)
 VERSION = '1.0 build89'
@@ -20,7 +23,7 @@ def ip_whitelist():
     #    else: signature = request.args['signature']
     #    return checkSignature(signature)
     #except: return False
-    return True
+    return bool(randint(0,1))
 #error pages
 @app.errorhandler(429)
 def ratelimit_handler(e): return render_template('ratelimit.html')
@@ -86,6 +89,47 @@ def getBMPreview(): return osuApi.getPreview(request)
 @app.route('/osu/api/v1/get-full', methods=['GET', 'POST'])
 def getBMFull(): return osuApi.getFull(request)
 
+###############
+# LOAD MODELS
+sa_t, sa_m = AutoTokenizer.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment"), AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment")
+ld_t, ld_m = AutoTokenizer.from_pretrained("papluca/xlm-roberta-base-language-detection"), AutoModelForSequenceClassification.from_pretrained("papluca/xlm-roberta-base-language-detection")
+
+##############
+# ANALYZE DATA API
+# to understand which text is negative, positive or neutral
+@app.route('/analyzeText/api/v1/sentiment', methods=['GET', 'POST'])
+def sentimentAnalys():
+    try:
+        text = request.form.get('text') or request.args.get('url') or ""
+        if text == "": return {"status": "error", "details": { "error_code": 101, "error_details": "No text provided" }}
+        
+        inputs = sa_t(text, return_tensors="pt")
+        
+        # Предсказание тональности текста
+        outputs = sa_m(**inputs)
+        logits = outputs.logits
+        predicted_sentiment_index = logits.argmax(dim=1).item()
+        predicted_sentiment = model.config.id2label[predicted_sentiment_index]
+    
+        return {"status": "pass", "predicted_sentiment": predicted_sentiment}
+    except: return {"status": "error", "details": { "error_code": 123, "error_details": str(e).replace("\n", " | ") }}
+
+@app.route('/analyzeText/api/v1/detectLang')
+def langDetect():
+    try:
+        text = request.form.get('text') or request.args.get('url') or ""
+        if text == "": return {"status": "error", "details": { "error_code": 101, "error_details": "No text provided" }}
+            
+        inputs = ld_t(text, return_tensors="pt")  
+        
+        outputs = ld_m(**inputs)
+        logits = outputs.logits
+        predicted_language_index = logits.argmax(dim=1).item()
+        predicted_language = model.config.id2label[predicted_language_index]
+        
+        return {"status": "pass", "predicted_language": predicted_language}
+    except: return {"status": "error", "details": { "error_code": 123, "error_details": str(e).replace("\n", " | ") }}
+
 if __name__ == "__main__":
     config = configFile()
     with open(config['config-path'], "w") as outfile: