Create app.py

app.py

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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()