# import requests
# import json
# import os

# # Your Hugging Face model URL
# API_URL = "sayyedAhmed/Crisis_Severity_Predictor_LSTM"  # Replace with your model's URL

# # Load your Hugging Face API token
# API_KEY = os.getenv("HF_API_KEY")  # Ensure the API key is stored in the environment or replace with the actual key

# headers = {
#     "Authorization": f"Bearer {API_KEY}",
#     "Content-Type": "application/json"
# }

# payload = {
#     "inputs": "Your test input here"  # Replace this with the actual input for your model
# }

# # Make the POST request to Hugging Face Inference API
# response = requests.post(API_URL, headers=headers, json=payload)

# # Print the response (the predictions)
# print(response.json())

from transformers import pipeline

# Specify the model you want to use
model_name = "sayyedAhmed/Crisis_Severity_Predictor_LSTM"

# Create the pipeline with manual framework specification (using 'tf' for TensorFlow)
classifier = pipeline("text-classification", model=model_name, framework="pt")

# Use the pipeline to run inference
result = classifier("Example text for classification.")
print(result)