updated

app.py

@@ -0,0 +1,81 @@
+import logging
+from flask import Flask, request, jsonify
+import os
+from wtforms import Form, StringField
+from wtforms.validators import DataRequired
+from config import model_ckpt, pipe, labels
+
+app = Flask(__name__)
+
+# # configure logging
+# logging.basicConfig(
+#     filename='app.log',
+#     level=logging.INFO,
+#     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+# )
+# logger = logging.getLogger(__name__)
+
+class PredictForm(Form):
+    text = StringField('text', [DataRequired()])
+
+def predict(text: str) -> dict:
+    """
+    Compute predictions for text.
+    :param text: str : The text to be analyzed.
+    :return: dict : A dictionary of predicted language and its score
+    """
+    try:
+        preds = pipe(text, return_all_scores=True, truncation=True, max_length=128)
+        if preds:
+            pred = preds[0]
+            pred = sorted(pred, key=lambda x: x['score'], reverse=True)
+            return {labels.get(p["label"],p["label"]): float(p["score"]) for p in pred[:1]}
+        else:
+            return {}
+    except Exception as e:
+        logger.error("Error processing request: %s", str(e))
+        return {'error': str(e)}, 500
+
+@app.route('/language', methods=['POST'])
+def predict_language():
+    """
+    A Language Prediction API which accepts 'text' as input and return the language of text along with score
+    ---
+    parameters:
+        - in: body
+          name: text
+          schema:
+            type: string
+            required: true
+          description: The text to be analyzed
+    responses:
+        200:
+            description: A JSON object containing the language and its score
+            schema:
+              type: object
+        400:
+            description: Invalid request
+        500:
+            description: Internal server error
+    """
+    # form = PredictForm(request.form)
+    # if form.validate():
+    text = request.json['text']
+    if not text:
+        return jsonify({'error': 'Empty text provided'}), 400
+
+    result = predict(text)
+    if result:
+        return jsonify(result)
+    else:
+        return jsonify({'error': 'No predictions found'}), 400
+    # else:
+    #     return jsonify({'error': 'Invalid input provided'}), 400
+
+if __name__ == '__main__':
+    log_file = 'app.log'
+    logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    logger = logging.getLogger(__name__)
+    logger.info("Running the app...")
+    app.run()
+

config.py

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+from transformers import pipeline
+
+model_ckpt = "papluca/xlm-roberta-base-language-detection"
+pipe = pipeline("text-classification", model=model_ckpt)
+
+labels = {"ar" : "Arabic", "bg" : "Bulgarian", "de" : "German", "el" : "Modern Greek", 
+"en" : "English", "es" : "Spanish", "fr" : "French", "hi" : "Hindi", "it" : "Italian", 
+"ja" : "Japanese", "nl" : "Dutch", "pl" : "Polish", "pt" : "Portuguese", "ru" : "Russian", 
+"sw" : "Swahili", "th" : "Thai", "tr" : "Turkish", "ur" : "Urdu", "vi" : "Vietnamese", "zh" : "Chinese"}

requirements.txt

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+torch==1.13.1
+WTForms==3.0.1
+transformers==4.25.1
+Flask==2.2.2