Commit
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ac25ae6
1
Parent(s):
02c7de4
Update app.py
Browse files
app.py
CHANGED
@@ -1,35 +1,30 @@
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# import streamlit as st
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# import torch as torch
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# from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
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import streamlit as st
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from transformers import pipeline
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from PIL import Image
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st.title("
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if file_name is not None:
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col1, col2 = st.columns(2)
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col1.image(image, use_column_width=True)
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predictions = pipeline(image)
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col2.header("Probabilities")
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for p in predictions:
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col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
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# tokenizer = AutoTokenizer.from_pretrained(model_name)
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# model = AutoModelForSequenceClassification.from_pretrained(model_name)
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#
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# encoded_input = tokenizer(text, return_tensors="pt")
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# output = model(**encoded_input)
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import streamlit as st
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import torch as torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
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from transformers import pipeline
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from PIL import Image
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model_name = "peace4ever/roberta-large-finetuned-mongolian_v3"
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pipeline = pipeline(task="sentiment-analysis", model=model_name)
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st.title("Эерэг? Сөрөг эсвэл аль нь ч биш?")
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text = st.text_area("Өгүүлбэр оруулна уу?")
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if text is not None:
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col1, col2 = st.columns(2)
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predictions = pipeline(text)
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col2.header("Probabilities")
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for p in predictions:
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col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
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# tokenizer = AutoTokenizer.from_pretrained(model_name)
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# model = AutoModelForSequenceClassification.from_pretrained(model_name)
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#
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# encoded_input = tokenizer(text, return_tensors="pt")
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# output = model(**encoded_input)
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