Update app.py

app.py

@@ -30,41 +30,10 @@ def bot(history):
     history[-1][1] = response
     return history
 
-"""
-
-Place holder alpaca model trained example:
-Required:
-!pip install -q datasets loralib sentencepiece
-!pip install -q git+https://github.com/zphang/transformers@c3dc391
-!pip install bitsandbytes
-
-"""
-
-'''
-
-tokenizer = LLaMATokenizer.from_pretrained("chavinlo/alpaca-native")
-
-model = LLaMAForCausalLM.from_pretrained(
-    "chavinlo/alpaca-native",
-    load_in_8bit=True,
-    device_map="auto",
-)
-'''
 
 ########## LOADING PRE-COMPUTED EMBEDDINGS ##########
 class_embeddings = pd.read_csv('Embeddings/MainClassEmbeddings.csv')
 
-"""
-abstract = """
-#Described herein are strength characteristics and biodegradation of articles produced using one or more “green” sustainable polymers and one or more carbohydrate-based polymers. A compatibilizer can optionally be included in the article. In some cases, the article can include a film, a bag, a bottle, a cap or lid therefore, a sheet, a box or other container, a plate, a cup, utensils, or the like.
-"""
-abstract= classification.clean_data(abstract, type='String')
-abstract_embedding = classification.sentence_embedder(abstract, 'Model_bert')
-Number = 10
-broad_scope_predictions = classification.broad_scope_class_predictor(class_embeddings, abstract_embedding, Number, Sensitivity='High')
-
-print(broad_scope_class_predictor)
-"""
 
 def classifier(userin):
     clean_in = classification.clean_data(userin, type='String')