Create handler.py

handler.py

@@ -0,0 +1,100 @@
+import subprocess
+import sys
+import torch
+import base64
+from io import BytesIO
+from PIL import Image
+import requests
+from transformers import AutoModelForCausalLM, AutoProcessor
+import os
+
+def install(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "--no-warn-script-location", package])
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        required_packages = ['timm', 'einops', 'flash-attn', 'Pillow','-U transformers']
+        for package in required_packages:
+            try:
+                install(package)
+                print(f"Successfully installed {package}")
+            except Exception as e:
+                print(f"Failed to install {package}: {str(e)}")
+        
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"Using device: {self.device}")
+        
+        self.model_name = "arjunanand13/LADP_Florence-60e"
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            trust_remote_code=True,
+        ).to(self.device)
+        
+        self.processor = AutoProcessor.from_pretrained(
+            self.model_name,
+            trust_remote_code=True,
+        )
+        
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    def process_image(self,image_data):
+        print("[DEBUG] Attempting to process image")
+        try:
+            # Check if image_data is a file path
+            if isinstance(image_data, str) and len(image_data) < 256 and os.path.exists(image_data):
+                with open(image_data, 'rb') as image_file:
+                    print("[DEBUG] File opened successfully")
+                    image = Image.open(image_file)
+            else:
+                # Assume image_data is base64 encoded
+                print("[DEBUG] Decoding base64 image data")
+                image_bytes = base64.b64decode(image_data)
+                image = Image.open(BytesIO(image_bytes))
+            
+            print("[DEBUG] Image opened with PIL:", image.format, image.size, image.mode)
+            return image
+        except Exception as e:
+            print(f"[ERROR] Error processing image: {str(e)}")
+            return None
+
+    def __call__(self, data):
+        try:
+            # Extract inputs from the expected Hugging Face format
+            inputs = data.pop("inputs", data)
+            
+            # Check if inputs is a dict or string
+            if isinstance(inputs, dict):
+                image_path = inputs.get("image", None)
+                text_input = inputs.get("text", "")
+            else:
+                # If inputs is not a dict, assume it's the image path
+                image_path = inputs
+                text_input = "What is in this image?"
+            print("[INFO]",image_path,text_input)
+            # Process image
+            image = self.process_image(image_path) if image_path else None
+            print("[INFO]",image)
+            # Prepare inputs for the model
+            model_inputs = self.processor(
+                images=image if image else None,
+                text=text_input,
+                return_tensors="pt"
+            )
+            
+            # Move inputs to device
+            model_inputs = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v 
+                           for k, v in model_inputs.items()}
+            
+            # Generate output
+            with torch.no_grad():
+                outputs = self.model.generate(**model_inputs)
+            
+            # Decode outputs
+            decoded_outputs = self.processor.batch_decode(outputs, skip_special_tokens=True)
+            print(f"[INFO],{decoded_outputs}")
+            print(f"[INFO],{decoded_outputs[0]}")
+            return {"generated_text": decoded_outputs[0]}
+        
+        except Exception as e:
+            return {"error": str(e)}