Linting, move env to community

environments/hack0/judgement_model.py

@@ -1,46 +0,0 @@
-import torch
-from transformers import CLIPProcessor, CLIPModel
-from PIL import Image
-import numpy as np
-
-class CLIPScorer:
-    def __init__(self, model_name="openai/clip-vit-base-patch32"):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        try:
-            self.model = CLIPModel.from_pretrained(model_name).to(self.device)
-            self.processor = CLIPProcessor.from_pretrained(model_name)
-            print(f"CLIPScorer initialized on {self.device} with {model_name}")
-        except Exception as e:
-            print(f"Error initializing CLIPModel: {e}. Ensure model name is correct and you have internet.")
-            self.model = None
-            self.processor = None
-            raise
-
-    @torch.no_grad() # Ensure no gradients are computed during inference
-    def score_images(self, images_np_list: list, target_text_description: str):
-        if not self.model or not self.processor:
-            print("CLIPScorer not properly initialized.")
-            return [0.0] * len(images_np_list) # Low score on error
-
-        try:
-            pil_images = [Image.fromarray(img_arr.astype(np.uint8)) for img_arr in images_np_list]
-            
-            inputs = self.processor(
-                text=[target_text_description], # Single text prompt
-                images=pil_images,
-                return_tensors="pt",
-                padding=True,
-                truncation=True
-            ).to(self.device)
-
-            outputs = self.model(**inputs)
-            image_text_similarity_scores = outputs.logits_per_image.squeeze().tolist() # Squeeze to remove the text dim
-
-            if not isinstance(image_text_similarity_scores, list): # If only one image
-                image_text_similarity_scores = [image_text_similarity_scores]
-
-            return image_text_similarity_scores
-        
-        except Exception as e:
-            print(f"Error in CLIP scoring: {e}")
-            return [0.0] * len(images_np_list) # Low score on error