Update app.py

app.py

@@ -1,54 +1,54 @@
-from flask import Flask, request, jsonify
-from classify_model import model, tokenizer
-import torch
-import numpy as np
-import os
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-import gdown
-from underthesea import word_tokenize
-
-
-if not os.path.exists("save_weights.pt"):
-    file_id = "1JeQ100QELbCCjCozF5SsHT1ca08Vvfuw"  
-    url = f"https://drive.google.com/uc?id={file_id}"
-    print("Downloading save_weights.pt from Google Drive...")
-    gdown.download(url, "save_weights.pt", quiet=False)
-    if not os.path.exists("save_weights.pt"):
-        raise FileNotFoundError("Failed to download save_weights.pt")
-
-model.load_state_dict(torch.load("save_weights.pt"))
-model.eval()
-
-app = Flask(__name__)
-
-def predict_toxic(sentence):
-    # tokens = rdrsegmenter.tokenize(sentence)
-    # statement = ""
-    # for token in tokens:
-    #     statement += " ".join(token)
-    # sentence = statement
-    sentence = word_tokenize(sentence, format="text")
-    sequence = tokenizer.encode(sentence)
-    while len(sequence) == 20:
-        sequence.insert(0, 0)
-    padded = torch.tensor([sequence])
-    with torch.no_grad():
-        preds = model(padded)
-    preds = np.argmax(preds.cpu().numpy(), axis=1)
-    return preds[0]
-
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    data = request.json
-    sentence = data.get('sentence', '')
-    if not sentence:
-        return jsonify({'error': 'No sentence provided'}), 400
-    result = predict_toxic(sentence)
-    print("Dự đoán:", result, type(result))
-    return jsonify({'toxic': int(result)})
-
-if __name__ == "__main__":
-    port = int(os.environ.get("PORT", 5000))
-    app.run(host="0.0.0.0", port=port)
+from flask import Flask, request, jsonify
+from classify_model import model, tokenizer
+import torch
+import numpy as np
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+import gdown
+from underthesea import word_tokenize
+
+
+if not os.path.exists("save_weights.pt"):
+    file_id = "1JeQ100QELbCCjCozF5SsHT1ca08Vvfuw"  
+    url = f"https://drive.google.com/uc?id={file_id}"
+    print("Downloading save_weights.pt from Google Drive...")
+    gdown.download(url, "save_weights.pt", quiet=False)
+    if not os.path.exists("save_weights.pt"):
+        raise FileNotFoundError("Failed to download save_weights.pt")
+
+model.load_state_dict(torch.load("save_weights.pt"))
+model.eval()
+
+app = Flask(__name__)
+
+def predict_toxic(sentence):
+    # tokens = rdrsegmenter.tokenize(sentence)
+    # statement = ""
+    # for token in tokens:
+    #     statement += " ".join(token)
+    # sentence = statement
+    sentence = word_tokenize(sentence, format="text")
+    sequence = tokenizer.encode(sentence)
+    while len(sequence) == 20:
+        sequence.insert(0, 0)
+    padded = torch.tensor([sequence])
+    with torch.no_grad():
+        preds = model(padded)
+    preds = np.argmax(preds.cpu().numpy(), axis=1)
+    return preds[0]
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json
+    sentence = data.get('sentence', '')
+    if not sentence:
+        return jsonify({'error': 'No sentence provided'}), 400
+    result = predict_toxic(sentence)
+    print("Dự đoán:", result, type(result))
+    return jsonify({'toxic': int(result)})
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 5001))
+    app.run(host="0.0.0.0", port=port)
     # app.run(host="0.0.0.0", port=5001)