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Update app.py
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app.py
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@@ -1,8 +1,29 @@
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import gradio as gr
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import pandas as pd
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import numpy as np
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
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# summary function - test for single gradio function interfrace
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def bulk_function(filename):
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@@ -17,6 +38,9 @@ def bulk_function(filename):
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def __getitem__(self, idx):
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return {k: v[idx] for k, v in self.tokenized_texts.items()}
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# load tokenizer and model, create trainer
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model_name = "j-hartmann/emotion-english-distilroberta-base"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# return dataframe for space output
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return YOUR_FILENAME
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gr.Interface(bulk_function,
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title="Emotion Classification from CSV",
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description="Upload csv file with 2 columns (in order): (a) ID column, (b) text column.
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allow_flagging=False,
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).launch(debug=True)
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import gradio as gr
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import pandas as pd
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import numpy as np
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import spacy
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from spacy import displacy
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
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def linkify():
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import pandas as pd
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import streamlit as st
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link1 = "https://stackoverflow.com/questions/71641666/hyperlink-in-streamlit-dataframe"
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link2 = "https://stackoverflow.com/questions/71731937/how-to-plot-comparison-in-streamlit-dynamically-with-multiselect"
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df = pd.DataFrame(
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{
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"url": [
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f'<a target="_blank" href="{link1}">Hyperlink in Streamlit dataframe</a>',
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f'<a target="_blank" href="{link2}">How to plot comparison in Streamlit dynamically with multiselect?</a>'
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],
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"label": ["question", "question"]
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}
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)
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doc=df.to_html(escape=False, index=False)
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html = displacy.render(doc, style="dep", page=True)
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return html
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# summary function - test for single gradio function interfrace
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def bulk_function(filename):
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def __getitem__(self, idx):
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return {k: v[idx] for k, v in self.tokenized_texts.items()}
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html = linkify()
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# load tokenizer and model, create trainer
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model_name = "j-hartmann/emotion-english-distilroberta-base"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# return dataframe for space output
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return YOUR_FILENAME
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gr.Interface(bulk_function,
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inputs=[gr.inputs.File(file_count="single", type="file", label="Upload file", optional=False),],
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outputs=[gr.outputs.File(label="Output file")],
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# examples=[["YOUR_FILENAME.csv"]], # computes, doesn't export df so far
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theme="huggingface",
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title="Emotion Classification from CSV",
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description="Upload csv file with 2 columns (in order): (a) ID column, (b) text column. Model: https://huggingface.co/j-hartmann/emotion-english-distilroberta-base.",
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allow_flagging=False,
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).launch(debug=True)
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