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index.py

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+import transformers
+from flask import Flask, request, jsonify
+from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline
+
+app = Flask(__name__)
+
+# Load model and tokenizer once at app startup
+tokenizer = RobertaTokenizerFast.from_pretrained("arpanghoshal/EmoRoBERTa")
+model = TFRobertaForSequenceClassification.from_pretrained("arpanghoshal/EmoROBERTa")
+emotion = pipeline("sentiment-analysis", model="arpanghoshal/EmoROBERTa")
+
+@app.route("/analyze_sentiment", methods=["POST"])
+def analyze_sentiment():
+    data = request.get_json()
+    phrase = data.get("phrase")
+
+    if phrase:
+        emotion_labels = emotion(phrase)
+        return jsonify({"emotion_labels": emotion_labels})
+    else:
+        return jsonify({"error": "Missing phrase"}), 400
+
+if __name__ == "__main__":
+    app.run(debug=True)  # Set debug=False in production

requirements.txt

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+flask
+transformers