Python code for running sentiment analysis on Zapier using the Inference API

sentiment-analysis-on-zapier.py

import requests

# Add the model ID and your Hugging Face Token
model = "cardiffnlp/twitter-roberta-base-sentiment-latest"
hf_token = "ADD_YOUR_HUGGING_FACE_TOKEN_HERE"

# Create the API request
API_URL = "https://api-inference.huggingface.co/models/" + model
headers = {"Authorization": "Bearer %s" % (hf_token)}
def analysis(payload):
  response = requests.post(API_URL, headers=headers, json=payload)
  while "is currently loading" in str(response.json()): # retry request while model loads
    print(response.json())
    time.sleep(30)
    response = requests.post(API_URL, headers=headers, json=payload)
  return response.json()

# Do the request to the Inference API and process the resutls
result = analysis({"inputs": str(input_data['input_data'])})
negative = result[0][0]['score']
neutral = result[0][1]['score']
positive = result[0][2]['score']

scores = [{'label': 'negative', 'score': negative}, {'label': 'neutral', 'score': neutral}, {'label': 'positive', 'score': positive}]
top_score = max(item['score'] for item in scores)
top_sentiment = next(item for item in scores if item["score"] == top_score)['label']

# Define the output for Zapier
output = [{'sentiment_label': top_sentiment, 'sentiment_score': top_score}]

I think Line 26 should be:
top_sentiment = next(item['label'] for item in scores if item["score"] == top_score)