Create handler.py

handler.py

@@ -0,0 +1,109 @@
+import subprocess
+import sys
+import torch
+import base64
+from io import BytesIO
+from PIL import Image
+import requests
+from transformers import AutoModelForCausalLM, AutoProcessor
+from tokenizers import Tokenizer, pre_tokenizers  
+import os
+
+def install(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "--no-warn-script-location", package])
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # Install necessary packages
+        required_packages = ['timm', 'einops', 'flash-attn', 'Pillow']
+        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}")
+
+        # Load the model
+        self.model_name = "arjunanand13/florence-enphaseall2-25e"
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_name, trust_remote_code=True
+        ).to(self.device)
+
+        # Manually load the tokenizer with a whitespace pre-tokenizer
+        self.tokenizer = self.load_tokenizer()
+
+        # Initialize the processor
+        self.processor = AutoProcessor.from_pretrained(self.model_name, trust_remote_code=True)
+
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    def load_tokenizer(self):
+        """Manually loads the tokenizer and adds a whitespace pre-tokenizer."""
+        try:
+            tokenizer = Tokenizer.from_pretrained(self.model_name)
+            tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
+            print("[INFO] Whitespace pre-tokenizer added.")
+            return tokenizer
+        except Exception as e:
+            print(f"[ERROR] Failed to load tokenizer: {str(e)}")
+            return None
+
+    def process_image(self, image_data):
+        """Processes image data from file path or base64-encoded string."""
+        print("[DEBUG] Attempting to process image")
+        try:
+            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:
+                print("[DEBUG] Decoding base64 image data")
+                image_bytes = base64.b64decode(image_data)
+                image = Image.open(BytesIO(image_bytes))
+
+            print("[DEBUG] Image opened:", 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):
+        """Processes input and generates model output."""
+        try:
+            inputs = data.pop("inputs", data)
+
+            if isinstance(inputs, dict):
+                image_path = inputs.get("image", None)
+                text_input = inputs.get("text", "")
+            else:
+                image_path = inputs
+                text_input = "What is in this image?"
+
+            print("[INFO] Image path:", image_path, "| Text input:", text_input)
+
+            image = self.process_image(image_path) if image_path else None
+
+            model_inputs = self.processor(
+                images=image if image else None,
+                text=text_input,
+                return_tensors="pt"
+            )
+
+            model_inputs = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v
+                            for k, v in model_inputs.items()}
+
+            with torch.no_grad():
+                outputs = self.model.generate(**model_inputs)
+
+            decoded_outputs = self.processor.batch_decode(outputs, skip_special_tokens=True)
+            print(f"[INFO] Generated text: {decoded_outputs[0]}")
+            return {"generated_text": decoded_outputs[0]}
+
+        except Exception as e:
+            print(f"[ERROR] {str(e)}")
+            return {"error": str(e)}
+