update

inference.py

@@ -1,28 +1,24 @@
-from huggingface_hub import InferenceClient
-from dotenv import load_dotenv
+import requests
+import json
 import os
 
-# Load the .env file to get environment variables
-load_dotenv()
+# Your Hugging Face model URL
+API_URL = "sayyedAhmed/Crisis_Severity_Predictor_LSTM"  # Replace with your model's URL
 
-# Get the API key from the environment variable
-api_key = os.getenv("HF_API_KEY")
+# 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
 
-# Initialize the InferenceClient with your API key
-client = InferenceClient(api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
+headers = {
+    "Authorization": f"Bearer {API_KEY}",
+    "Content-Type": "application/json"
+}
 
-# Define the model path or model name on Hugging Face
-model_name = "sayyedAhmed/Crisis_Severity_Predictor_LSTM"
+payload = {
+    "inputs": "Your test input here"  # Replace this with the actual input for your model
+}
 
-# Define the input data (make sure this matches your model's expected input format)
-# For example, this could be a list of features for one instance
-input_data = [[1.23, 4.56, 7.89, 10.11]]  # Adjust this based on your model's input format
+# Make the POST request to Hugging Face Inference API
+response = requests.post(API_URL, headers=headers, json=payload)
 
-# Request inference (you can customize this request as needed, such as adding more parameters)
-predictions = client.predict(
-    model=model_name,
-    inputs=input_data
-)
-
-# Print out the predictions
-print("Predictions:", predictions)
+# Print the response (the predictions)
+print(response.json())