File size: 2,655 Bytes
ed9dc6d bcdcc8b ed9dc6d 632553d ed9dc6d bcdcc8b ed9dc6d f1b215c |
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 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
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(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'])
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(predict_category)
st.dataframe(df)
st.sidebar.header("Download Results")
st.sidebar.text("Download the tagged results as a CSV file.")
# Create a download button
if st.sidebar.button("Download"):
csv = df.to_csv(index=False)
b64 = base64.b64encode(csv.encode()).decode()
# Set the Content-Disposition header to force the download
headers = {"Content-Disposition": "attachment; filename=results.csv"}
# Use `st.download_file()` to download the file
st.download_file(b64, "results.csv", headers=headers)
|