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Upload app.py
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app.py
CHANGED
@@ -36,11 +36,11 @@ def perform_asde_inference(text, dataset, model_id):
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random_i = np.random.randint(low=0, high=df.shape[0], size=(1,)).flat[0]
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selected_df = df.iloc[random_i]
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text = selected_df['clean_text']
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print(type(selected_df['actual_aspects']))
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print(type(selected_df['actual_sentiments']))
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print(selected_df['actual_aspects'])
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true_aspect = selected_df['actual_aspects']
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true_sentiment = selected_df['actual_sentiments']
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bos_instruction = """Definition: The output will be the aspects (both implicit and explicit) and the aspects sentiment polarity. In cases where there are no aspects the output should be noaspectterm:none.
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Positive example 1-
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@@ -74,11 +74,6 @@ def perform_asde_inference(text, dataset, model_id):
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pred_doubles = pd.DataFrame(list(map(list, zip(pred_asp, pred_sent))),columns=['Aspect','Sentiment'])
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if not text:
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true_doubles = pd.DataFrame(list(map(list, zip(ast.literal_eval(true_aspect), ast.literal_eval(true_sentiment)))),columns=['Aspect','Sentiment'])
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else:
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true_doubles = pd.DataFrame([["",""]],columns=['Aspect','Sentiment'])
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return pred_doubles, true_doubles, text, model_generated
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def run_demo(text, dataset, model_id):
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random_i = np.random.randint(low=0, high=df.shape[0], size=(1,)).flat[0]
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selected_df = df.iloc[random_i]
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text = selected_df['clean_text']
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true_aspect = selected_df['actual_aspects']
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true_sentiment = selected_df['actual_sentiments']
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true_doubles = pd.DataFrame(list(map(list, zip(ast.literal_eval(true_aspect), ast.literal_eval(true_sentiment)))),columns=['Aspect','Sentiment'])
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else:
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true_doubles = pd.DataFrame([["",""]],columns=['Aspect','Sentiment'])
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bos_instruction = """Definition: The output will be the aspects (both implicit and explicit) and the aspects sentiment polarity. In cases where there are no aspects the output should be noaspectterm:none.
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Positive example 1-
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pred_doubles = pd.DataFrame(list(map(list, zip(pred_asp, pred_sent))),columns=['Aspect','Sentiment'])
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return pred_doubles, true_doubles, text, model_generated
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def run_demo(text, dataset, model_id):
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