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app.py

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+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from azure.storage.blob import BlobServiceClient
+from flask import Flask, request, jsonify
+
+app = Flask(__name__)
+
+# BERT model and tokenizer
+model_name = "textattack/bert-base-uncased-yelp-polarity"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Predict the category
+def predict_category(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    probabilities = logits.softmax(dim=1)
+    predicted_category = ["Documentation", "Content", "Memes"][torch.argmax(probabilities)]
+    return predicted_category
+
+# Function to extract text from JSON and predict the category
+def predict_category_from_json(json_data):
+    input_text = json_data.get('text', '')
+    category = predict_category(input_text)
+    return category
+
+# Importing data from blob storage
+def import_data_from_blob(blob_service_client, container_name, blob_name):
+    blob_client = blob_service_client.get_blob_client(container=container_name, blob=blob_name)
+    blob_data = blob_client.download_blob()
+    content = blob_data.readall()
+    return content
+
+@app.route('/predict_category', methods=['POST'])
+def predict_category_api():
+    try:
+        # Assuming JSON format with a key named 'text' that contains the text data.
+        json_data = request.get_json()
+        input_text = json_data.get('text', '')
+
+        # Predict the category
+        category = predict_category(input_text)
+
+        response = {'category': category}
+        return jsonify(response)
+    except Exception as e:
+        return jsonify({'error': str(e)})
+
+if __name__ == '__main__':
+    # Azure Blob Storage connection string
+    connection_string = "DefaultEndpointsProtocol=https;AccountName=keywisestorage;AccountKey=uRzlCQwv/SSF6WgkEz0g83dBjnFrziSNNt8PIY5Nnt+OJic0v5xjPnO8ZMhb7SjyesYSOK79TbJ/+AStdLKiDw==;EndpointSuffix=core.windows.net"
+    blob_service_client = BlobServiceClient.from_connection_string(connection_string)
+
+    # Define your container and blob name
+    container_name = "keywisestorage"
+    blob_name = "pagescontainer"
+
+    app.run(host="0.0.0.0", port=5000)