Delete modules/beautify_model.py

modules/beautify_model.py

@@ -1,99 +0,0 @@
-import os
-import io,copy,requests,spaces,gradio as gr,numpy as np
-from transformers import T5ForConditionalGeneration, T5Tokenizer
-
-LAMINI_PROMPT_LONG= "gokaygokay/Lamini-Prompt-Enchance-Long"
-
-class beautify_class:
-    def __init__(self, repoId: str, device: str = None, loadModel: bool = False):
-        self.modelPath = self.download_model(repoId)
-        if device is None:
-            import torch
-            self.totalVram = 0
-            if torch.cuda.is_available():
-                try:
-                    deviceId = torch.cuda.current_device()
-                    self.totalVram = torch.cuda.get_device_properties(deviceId).total_memory / (1024 * 1024 * 1024)
-                except Exception as e:
-                    print(traceback.format_exc())
-                    print("Error detect vram: " + str(e))
-                device = "cuda" if self.totalVram > (8 if "8B" in repoId else 4) else "cpu"
-            else:
-                device = "cpu"
-        self.device = device
-        self.system_prompt = "Summarize, beautify and enhance the following English labels describing a single image into a readable English article:\n\n"
-        if loadModel:
-            self.load_model()
-
-    def download_model(self, repoId):
-        import huggingface_hub
-        allowPatterns = [
-            #"tf_model.h5",
-            #"model.ckpt.index",
-            #"flax_model.msgpack",
-            #"pytorch_model.bin",
-            "config.json",
-            "generation_config.json",
-            "model.safetensors",
-            "tokenizer.json",
-            "tokenizer_config.json",
-            "special_tokens_map.json",
-            "vocab.json",
-            "added_tokens.json",
-            "spiece.model"
-        ]
-        kwargs = {"allow_patterns": allowPatterns,}
-        try:
-            return huggingface_hub.snapshot_download(repoId, **kwargs)
-        except (huggingface_hub.utils.HfHubHTTPError, requests.exceptions.ConnectionError) as exception:
-            import warnings
-            warnings.warn(
-                "An error occurred while synchronizing the model %s from the Hugging Face Hub:\n%s",
-                repoId,
-                exception,
-            )
-            warnings.warn(
-                "Trying to load the model directly from the local cache, if it exists."
-            )
-            kwargs["local_files_only"] = True
-            return huggingface_hub.snapshot_download(repoId, **kwargs)
-
-    def load_model(self):
-        import transformers
-        try:
-            print('\n\nLoading model: %s\n\n' % self.modelPath)
-            self.Tokenizer = T5Tokenizer.from_pretrained(self.modelPath)
-            self.Model = T5ForConditionalGeneration.from_pretrained(self.modelPath).to(self.device)
-        except Exception as e:
-            self.release_vram()
-            raise e
-
-    def release_vram(self):
-        try:
-            import torch
-            if torch.cuda.is_available():
-                if getattr(self, "Model", None) is not None:
-                    self.Model.to('cpu')
-                    del self.Model
-                if getattr(self, "Tokenizer", None) is not None:
-                    del self.Tokenizer
-                import gc
-                gc.collect()
-                torch.cuda.empty_cache()
-                print("release vram end.")
-        except Exception as e:
-            print(traceback.format_exc())
-            print("Error release vram: " + str(e))
-
-    def beautify(self, text: str, max_length: int = 400):
-        try:
-            input_ids = self.Tokenizer(self.system_prompt + text, return_tensors="pt").input_ids.to(self.device)
-            output = self.Model.generate(input_ids, max_length=max_length, no_repeat_ngram_size=3, num_beams=2, early_stopping=True)
-            result = self.Tokenizer.decode(output[0], skip_special_tokens=True)
-            return result
-        except Exception as e:
-            print(traceback.format_exc())
-            print("Error found: " + str(e))
-            return None
-
-beautify_list=[LAMINI_PROMPT_LONG]