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