Create handler.py

handler.py

@@ -0,0 +1,62 @@
+import json
+from transformers import pipeline
+
+class EndpontHandler():
+    def __init__(self):
+        """
+        Initialize the handler by setting up the summarization pipeline.
+        """
+        self.summarizer = None
+
+    def load_model(self, model_dir):
+        """
+        Load the summarization pipeline with the fine-tuned model.
+        
+        Args:
+            model_dir (str): The directory where the fine-tuned model is stored.
+        """
+        print("Loading summarization pipeline...")
+        self.summarizer = pipeline("summarization", model=model_dir)
+
+    def __call__(self, inputs):
+        """
+        Handle incoming requests by processing input text and returning the summary.
+
+        Args:
+            inputs (str): JSON string with "text" key containing the input text.
+
+        Returns:
+            str: JSON string containing the "summary" key with the generated summary.
+        """
+        try:
+            # Parse input JSON
+            input_data = json.loads(inputs)
+            input_text = input_data.get("text", "")
+
+            if not input_text:
+                return json.dumps({"error": "No input text provided."})
+
+            # Prepend custom instruction to input text
+            modified_text = f"summarize in one paragraph: {input_text}"
+
+            # Generate the summary
+            summary = self.summarizer(
+                modified_text,
+                truncation=True,
+                max_length=150,
+                min_length=30
+            )[0]["summary_text"]
+
+            # Return the summary in JSON format
+            return json.dumps({"summary": summary})
+
+        except Exception as e:
+            # Handle unexpected errors
+            return json.dumps({"error": str(e)})
+
+
+# Example usage:
+# handler = CustomHandler()
+# handler.load_model("path_to_your_model_directory")
+# result = handler(json.dumps({"text": "Your input text here"}))
+# print(result)