Update app.py

app.py

@@ -18,10 +18,10 @@ sentiment_model = pipeline("text-classification", model="AhmedTaha012/managersFe
 increase_decrease_model = pipeline("text-classification", model="AhmedTaha012/nextQuarter-status-V1.1.9")
 tokenizerTopic = AutoTokenizer.from_pretrained("nickmuchi/finbert-tone-finetuned-finance-topic-classification",use_fast=True,token="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")
 modelTopic = AutoModelForSequenceClassification.from_pretrained("nickmuchi/finbert-tone-finetuned-finance-topic-classification",token="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")
-torch.compile(modelTopic)
+# torch.compile(modelTopic)
 tokenizer = AutoTokenizer.from_pretrained("AhmedTaha012/finance-ner-v0.0.9-finetuned-ner")
 model = AutoModelForTokenClassification.from_pretrained("AhmedTaha012/finance-ner-v0.0.9-finetuned-ner")
-torch.compile(model)
+# torch.compile(model)
 # torch.compile(model)
 nlpPipe = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
 def getSpeakers(data):
@@ -190,7 +190,7 @@ def convert_amount_to_number(amount_str):
     except ValueError:
         return 0  # Return 0 if the conversion fails
 def getTopic(encoded_input):
-    modelTopic.to("cuda")
+    # modelTopic.to("cuda")
     with torch.no_grad():
         logits = modelTopic(**encoded_input).logits
     predicted_class_id = logits.argmax().item()
@@ -220,7 +220,7 @@ def selectedCorpusForNextQuarterModel(x,quarter,year):
     result=[x for x in result if len(x)>10]
     des=f"the {number_word_dict[str(quarter)]} quarter results of the {year}"
     courpus=result
-    embeddings_1 = similarityModel.encode([des]+courpus, normalize_embeddings=True,device='cuda',show_progress_bar=False) 
+    embeddings_1 = similarityModel.encode([des]+courpus, normalize_embeddings=True,show_progress_bar=False) 
     sents=[des]+courpus
     rest=[sents[f] for f in [list(cosine_similarity(embeddings_1)[0][1:]).index(value)+1 for value in sorted(list(cosine_similarity(embeddings_1)[0][1:]),reverse=True)][:3]]
     return ",".join(rest)