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| # imports | |
| from transformers import pipeline | |
| import gradio as gr | |
| import pandas as pd | |
| # define nlp mask | |
| model = "siebert/sentiment-roberta-large-english" | |
| nlp = pipeline(model=model, device=0) # set device=0 to use GPU (CPU default, -1) | |
| # perform inference on given file | |
| def inference(df, filename): | |
| # texts & ids | |
| texts = df[df.columns[1]].to_list() | |
| ids = df[df.columns[0]].to_list() | |
| # create new df based on csv inputs | |
| new_df = pd.DataFrame(columns=[df.columns[0], df.columns[1], "Label", "Score"]) | |
| # iterate over texts, perform inference | |
| for index in range(len(texts)): | |
| preds = nlp(texts[index]) | |
| pred_sentiment = preds[0]["label"] | |
| pred_score = preds[0]["score"] | |
| print(texts[index]) | |
| print(preds) | |
| # write data into df | |
| # predicted sentiment | |
| new_df.at[index, "Label"] = pred_sentiment | |
| # predicted score | |
| new_df.at[index, "Score"] = pred_score | |
| # write text | |
| new_df.at[index, df.columns[1]] = texts[index] | |
| # write ID | |
| new_df.at[index, df.columns[0]] = ids[index] | |
| # export new file | |
| n_filename = filename.name.split(".")[0] + "_csiebert_sentiment.csv" | |
| new_df.to_csv(n_filename, index=False) | |
| # return new file | |
| return n_filename | |
| # handle file reading for both csv and excel files | |
| def read_file(filename): | |
| # check type of input file | |
| if filename.name.split(".")[1] == "csv": | |
| print("entered") | |
| # read file, drop index if exists | |
| df = pd.read_csv(filename.name, index_col=False) | |
| # perform inference on given .csv file | |
| result = inference(df=df, filename=filename) | |
| print("computed") | |
| return result | |
| elif filename.name.split(".")[1] == "xlsx": | |
| df = pd.read_excel(filename.name, index_col=False) | |
| # handle Unnamed | |
| if df.columns[0] == "Unnamed: 0": | |
| df = df.drop("Unnamed: 0", axis=1) | |
| # perform inference on given .xlsx file | |
| result = inference(df=df, filename=filename) | |
| return result | |
| # if neither csv nor xlsx provided -> exit | |
| else: | |
| return | |
| gr.Interface(read_file, | |
| inputs=[gr.inputs.File(label="Input file")], | |
| outputs=[gr.outputs.File(label="Output file")], | |
| description="Sentiment analysis: Input a csv/xlsx of form ID, Text. App performs sentiment analysis on Texts and exports results as new csv to download.", | |
| allow_flagging=False, | |
| layout="horizontal", | |
| ).launch() |