Create filehandler.py

filehandler.py

@@ -0,0 +1,41 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+class FileHandler:
+    def __init__(self, model_path):
+        self.model_path = model_path
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.model = AutoModelForCausalLM.from_pretrained(model_path)
+        self.model.eval()
+
+    def generate_text(self, prompt, max_length=100, num_return_sequences=1, temperature=0.7):
+        input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
+
+        generated_ids = self.model.generate(
+            input_ids,
+            max_length=max_length,
+            num_return_sequences=num_return_sequences,
+            temperature=temperature,
+            pad_token_id=self.tokenizer.eos_token_id,
+        )
+
+        generated_texts = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in generated_ids]
+        return generated_texts
+
+    def __call__(self, request):
+        # Parse the request and extract the necessary information
+        prompt = request["prompt"]
+        max_length = request.get("max_length", 100)
+        num_return_sequences = request.get("num_return_sequences", 1)
+        temperature = request.get("temperature", 0.7)
+
+        # Generate text based on the prompt and parameters
+        generated_texts = self.generate_text(prompt, max_length, num_return_sequences, temperature)
+
+        # Prepare the response
+        response = {
+            "generated_texts": generated_texts
+        }
+
+        return response
+
+handler = FileHandler(".")