Upload app.py

app.py

@@ -0,0 +1,39 @@
+from flask import Flask, request, jsonify, render_template
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+app = Flask(__name__)
+
+# Load the model and tokenizer
+model = BertForSequenceClassification.from_pretrained('./model1')
+tokenizer = BertTokenizer.from_pretrained('./model1')
+
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    pred = torch.argmax(probs).item()
+    
+    if pred == 0:
+        sentiment = "negative"
+    elif pred == 1:
+        sentiment = "neutral"
+    else:
+        sentiment = "positive"
+        
+    return sentiment
+    return sentiment
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json
+    review = data['review']
+    sentiment = predict_sentiment(review)
+    return jsonify({'sentiment': sentiment})
+
+if __name__ == '__main__':
+    app.run(debug=True)