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from transformers import pipeline |
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import numpy as np |
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import torch |
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import transformers |
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import json |
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import pandas as pd |
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from numpy.random import seed |
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seed(1) |
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import emoji |
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import string |
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import nltk |
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from nltk.corpus import stopwords |
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from nltk.stem import PorterStemmer |
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from nltk.stem import WordNetLemmatizer |
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import re |
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stemmer = PorterStemmer() |
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nltk.download('wordnet') |
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nltk.download('omw-1.4') |
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nltk.download('stopwords') |
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lemmatizer = WordNetLemmatizer() |
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stopwords = nltk.corpus.stopwords.words('english') |
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import gradio as gr |
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pipe = pipeline("text-classification", model="dsmsb/16class_12k_newtest1618_xlm_roberta_base_27nov_v2_8epoch") |
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def classify(text): |
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output = pipe(return,top_k = 2) |
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return {"class":output} |
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inputs = gr.inputs.Textbox(label="pdf link") |
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outputs = gr.outputs.Textbox(label="OCR Text") |
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demo = gr.Interface(fn=classify,inputs=inputs,outputs=outputs) |
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demo.launch() |
