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import streamlit as st
import pandas as pd
import pickle
import requests
import base64
#===config===
st.set_page_config(
page_title="ETDs Tagging",
page_icon="",
layout="wide"
)
st.header("Tagging Categories")
st.subheader('Put your file here...')
#========unique id========
@st.cache_resource(ttl=3600)
def create_list():
l = [1, 2, 3]
return l
l = create_list()
first_list_value = l[0]
l[0] = first_list_value + 1
uID = str(l[0])
@st.cache_data(ttl=3600)
def get_ext(uploaded_file):
extype = uID+uploaded_file.name
return extype
@st.cache_resource(ttl=3600)
# def read_model(local_path):
# with open(local_path, "rb") as f:
# svm_classifier = pickle.load(f)
# return svm_classifier
# def read_tf(local_path):
# with open(local_path, "rb") as f:
# preprocessing = pickle.load(f)
# return preprocessing
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/category/model2.pkl")
preprocessing = read_tf("https://github.com/manika-lamba/ml/raw/main/category/preprocessing.pkl")
# 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
# Create sidebar
#===upload file===
@st.cache_data(ttl=3600)
def upload(file):
papers = pd.read_csv(uploaded_file)
return papers
@st.cache_data(ttl=3600)
def conv_txt(extype):
papers = pd.read_csv(uploaded_file, sep='\t', lineterminator='\r')
papers.rename(columns=col_dict, inplace=True)
return papers
# Read Data
uploaded_file = st.file_uploader("Choose a file", type=['csv'])
st.sidebar.header("Download Results")
st.sidebar.text("Download the tagged results as a CSV file.")
if uploaded_file is not None:
df = pd.read_csv(uploaded_file, encoding='latin-1')
st.dataframe(df)
# Tag the "Abstract" column with the corresponding categories
df['category'] = df['Abstract'].apply(lambda x: predict_category(x)[0])
st.dataframe(df)
# Convert DataFrame to CSV and encode it properly
csv = df.to_csv(index=False).encode('utf-8')
b64 = base64.b64encode(csv).decode() # Decode the Base64 encoded bytes
st.sidebar.download_button(
label="Download CSV",
data=base64.b64decode(b64), # Decode Base64 to bytes
file_name="results.csv",
mime="text/csv",
key='download-csv'
) |