| # 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) | |