Create handler.py

handler.py

@@ -0,0 +1,62 @@
+from typing import Dict, List, Any
+import torch
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration, BitsAndBytesConfig
+from PIL import Image
+import requests
+from io import BytesIO
+import re
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Configuració de la quantització
+        quantization_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.float16,
+        )
+
+        # Carrega el processador i model de forma global
+        self.processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
+        self.model = LlavaNextForConditionalGeneration.from_pretrained(
+            "llava-hf/llava-v1.6-mistral-7b-hf",
+            quantization_config=quantization_config,
+            device_map="auto"
+        )
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        image_url = data.get("url")
+        prompt = data.get("prompt")
+        
+        try:
+            response = requests.get(image_url, stream=True)
+            image = Image.open(response.raw)
+            
+            if image.format == 'PNG':
+                image = image.convert('RGB')
+                buffer = BytesIO()
+                image.save(buffer, format="JPEG")
+                buffer.seek(0)
+                image = Image.open(buffer)
+
+        except Exception as e:
+            return {"error": str(e)}
+
+        inputs = self.processor(prompt, image, return_tensors="pt").to("cuda")
+        output = self.model.generate(**inputs, max_new_tokens=100)
+        result = self.processor.decode(output[0], skip_special_tokens=True)
+        
+        scores = self.extract_scores(result)
+        sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True)
+        return sorted_scores
+
+    def extract_scores(self, response):
+        scores = {}
+        result_part = response.split("[/INST]")[-1].strip()
+        pattern = re.compile(r'(\d+)\.\s*(.*?):\s*(\d+)')
+        matches = pattern.findall(result_part)
+        for match in matches:
+            category_number = int(match[0])
+            category_name = match[1].strip()
+            score = int(match[2])
+            scores[category_name] = score
+        return scores