Create handler.py

handler.py

@@ -0,0 +1,32 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer globally
+MODEL_NAME = "abhijsrwala/lora_model"
+
+def load_model():
+    # Load the model and tokenizer
+    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    return model, tokenizer
+
+# Load model once to avoid reloading on every request
+model, tokenizer = load_model()
+
+def handle_request(input_data):
+    """
+    Handles inference requests.
+    Args:
+        input_data (str): The input text prompt.
+    Returns:
+        str: The model's response.
+    """
+    # Tokenize the input text
+    inputs = tokenizer.encode(input_data, return_tensors="pt")
+
+    # Generate text
+    outputs = model.generate(inputs, max_length=200, num_return_sequences=1)
+    
+    # Decode the output
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response