Upload 2 files

app.py

@@ -5,17 +5,17 @@ from compressor import PromptCompressor
 def compressit(original_text, compressor1, ratio, maxlength):
 
     if compressor1=="Selective Context":
-        compressor = PromptCompressor(type='SCCompressor', lang='en', model='gpt2', device='cuda')
+        compressor = PromptCompressor(type='SCCompressor', lang='en', model='gpt2', device='cpu')
     elif compressor1=="LLMLingua":
         return "Sorry, currently we cannot provide services for LLMLingua due to the Huggingface Token issue. Please try other compressors."
     elif compressor1=="LongLLMLingua":
         return "Sorry, currently we cannot provide services for LongLLMLingua due to the Huggingface Token issue. Please try other compressors."
     elif compressor1=="SCRL":
-        compressor = PromptCompressor(type='SCRLCompressor', model_dir="models/gigaword-L8/", device="cuda", tokenizer_dir="sentence-transformers/paraphrase-distilroberta-base-v2")
+        compressor = PromptCompressor(type='SCRLCompressor', model_dir="models/gigaword-L8/", device="cpu", tokenizer_dir="sentence-transformers/paraphrase-distilroberta-base-v2")
     elif compressor1=="KiS":
         compressor = PromptCompressor(type='KiSCompressor', device="cuda", model_dir="philippelaban/keep_it_simple")
     else:
-        compressor = PromptCompressor(type='SCCompressor', lang='en', model='gpt2', device='cuda')
+        compressor = PromptCompressor(type='SCCompressor', lang='en', model='gpt2', device='cpu')
 
     if compressor1 != "SCRL":
         compressed_prompt = compressor.compressgo(original_prompt=original_text, ratio=float(ratio), max_length=int(maxlength))

requirements.txt

@@ -20,4 +20,5 @@ wandb
 rake-nltk
 scikit-learn
 python-Levenshtein
-colorama
+colorama
+en_core_web_sm