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from flask import Flask, request, jsonify
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification
# Initialize the tokenizer and model
app = Flask(__name__)
classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
@app.route('/classify', methods=['POST'])
def classify():
try:
data = request.get_json()
if 'text' not in data:
return jsonify({"error": "Missing 'text' field"}), 400
text = data['text']
result = classifier(text)
return jsonify(result)
except Exception as e:
return jsonify({"error": str(e)}), 500
tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
@app.route('/ner', methods=['POST'])
def ner_endpoint():
try:
# Get text from request
data = request.get_json()
text = data.get("text", "")
# Perform NER
ner_results = nlp(text)
# Extract words and their corresponding entities
words_and_entities = [
{"word": result['word'], "entity": result['entity']}
for result in ner_results
]
# Return JSON response with the words and their entities
return jsonify({"entities": words_and_entities})
except Exception as e:
return jsonify({"error": str(e)}), 500
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