File size: 2,203 Bytes
49f942d bb0a66f 2afd8e2 bb0a66f 196a9c0 bb0a66f 4666190 bb0a66f 333facd bb0a66f 4666190 bb0a66f 4666190 83574a2 1d9dd26 4666190 1d9dd26 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
from sklearn.model_selection import train_test_split
import streamlit as st
import pandas as pd
import pickle
import requests
import base64
@st.cache_data(ttl=3600)
def read_model(url):
response = requests.get(url)
open("temp.pkl", "wb").write(response.content)
with open("temp.pkl", "rb") as f:
svm_classifier = pickle.load(f)
return svm_classifier
def read_tf(url):
response = requests.get(url)
open("temp.pkl", "wb").write(response.content)
with open("temp.pkl", "rb") as f:
preprocessing = pickle.load(f)
return preprocessing
svm_classifier = read_model("https://github.com/manika-lamba/ml/raw/main/model2.pkl")
preprocessing = read_tf("https://github.com/manika-lamba/ml/raw/main/preprocessing.pkl")
# Create sidebar
# Create tab for choosing CSV file
st.sidebar.header("Choose CSV File with 'Abstract' field")
uploaded_file = st.sidebar.file_uploader("", type=["csv"])
st.sidebar.header("Download Results")
st.sidebar.text("Download the tagged results as a CSV file.")
st.title("About")
st.subheader("You can tag your input CSV file of theses and dissertations with Library Science, Archival Studies, and Information Science categories. The screen will show the output.")
tab1, tab2, tab3 = st.tabs(["📈 Load Data", "📃 Tagged ETDs", "📓 Download Data"])
with tab1:
#===load data===
if uploaded_file is not None:
df = pd.read_csv(uploaded_file, encoding='latin-1')
st.dataframe(df)
with tab2:
#===tagged ETDs===
# Tag the "Abstract" column with the corresponding categories
df['category'] = df['Abstract'].apply(predict_category)
st.dataframe(df)
# Function to predict the category for a given abstract
def predict_category(abstract):
# Preprocess the abstract
abstract_preprocessed = preprocessing.transform([abstract])
# Make prediction
prediction = svm_classifier.predict(abstract_preprocessed)
return prediction
with tab3:
#===download result===
# Create a download button
if st.sidebar.button("Download"):
csv = df.to_csv(index=False)
b64 = base64.b64encode(csv.encode()).decode()
href = f'<a href="data:file/csv;base64,{b64}" download="results.csv">Download csv file</a>'
st.markdown(href, unsafe_allow_html=True) |