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import streamlit as st |
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import numpy as np |
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import pandas as pd |
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import json |
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import base64 |
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import uuid |
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from pandas import DataFrame |
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import time |
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import re |
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def download_button(object_to_download, download_filename, button_text): |
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if isinstance(object_to_download, bytes): |
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pass |
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elif isinstance(object_to_download, pd.DataFrame): |
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object_to_download = object_to_download.to_csv(index=False) |
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else: |
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object_to_download = json.dumps(object_to_download) |
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try: |
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b64 = base64.b64encode(object_to_download.encode()).decode() |
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except AttributeError as e: |
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b64 = base64.b64encode(object_to_download).decode() |
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button_uuid = str(uuid.uuid4()).replace("-", "") |
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button_id = re.sub("\d+", "", button_uuid) |
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custom_css = f""" |
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<style> |
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#{button_id} {{ |
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display: inline-flex; |
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align-items: center; |
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justify-content: center; |
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background-color: rgb(255, 255, 255); |
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color: rgb(38, 39, 48); |
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padding: .25rem .75rem; |
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position: relative; |
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text-decoration: none; |
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border-radius: 4px; |
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border-width: 1px; |
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border-style: solid; |
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border-color: rgb(230, 234, 241); |
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border-image: initial; |
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}} |
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#{button_id}:hover {{ |
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border-color: rgb(246, 51, 102); |
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color: rgb(246, 51, 102); |
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}} |
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#{button_id}:active {{ |
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box-shadow: none; |
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background-color: rgb(246, 51, 102); |
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color: white; |
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}} |
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</style> """ |
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dl_link = ( |
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custom_css |
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+ f'<a download="{download_filename}" id="{button_id}" href="data:file/txt;base64,{b64}">{button_text}</a><br><br>' |
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) |
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st.markdown(dl_link, unsafe_allow_html=True) |
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class c_model: |
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def __init__(self): |
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pass |
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@st.cache |
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def load_model(self, name_or_path): |
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time.sleep(3) |
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return None |
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def predict(self, texts): |
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return np.random.randint(2), np.random.rand() |
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st.title('Sentiment Analysis') |
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with st.spinner('Loading classification model...'): |
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from transformers import pipeline |
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checkpoint = "amir7d0/distilbert-base-uncased-finetuned-amazon-reviews" |
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classifier = pipeline("text-classification", model=checkpoint) |
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tab1, tab2 = st.tabs(["Single Comment", "Multiple Comment"]) |
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with tab1: |
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st.subheader('Single comment classification') |
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text_input = st.text_area(label='Paste your text below (max 256 words)', |
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value='Hiiiiiiiii') |
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MAX_WORDS = 256 |
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res = len(re.findall(r"\w+", text_input)) |
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if res > MAX_WORDS: |
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st.warning( |
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"⚠️ Your text contains " |
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+ str(res) |
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+ " words." |
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+ " Only the first 256 words will be reviewed! 😊" |
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) |
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text_input = text_input[:MAX_WORDS] |
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submit_button = st.button(label='Submit comment') |
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if submit_button: |
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with st.spinner('Predicting ...'): |
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start_time = time.time() |
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time.sleep(2) |
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preds = classifier([text_input])[0] |
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end_time = time.time() |
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p_time = round(end_time-start_time, 2) |
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st.success(f'Prediction finished in {p_time}s!') |
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st.write(f'Label: {preds["label"]}, with certainty: {preds["score"]}') |
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with tab2: |
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st.subheader('Multiple comment classification') |
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file_input = st.file_uploader(label='Choose a file:', type='csv') |
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if file_input: |
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try: |
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df = pd.read_csv(file_input) |
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texts = df['text'].to_list() |
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except: |
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st.write('Bad File Error...') |
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st.write(f"First 5 rows of {file_input.name} texts") |
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st.write(texts[:5]) |
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submit_button = st.button(label='Submit file') |
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if submit_button: |
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with st.spinner('Predicting ...'): |
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start_time = time.time() |
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time.sleep(2) |
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preds = classifier(texts) |
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end_time = time.time() |
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p_time = round(end_time-start_time, 2) |
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st.success(f'Prediction finished in {p_time}s!') |
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for text, pred in zip(texts, preds): |
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pred['text'] = text |
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c1, c2 = st.columns([3, 1]) |
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with c1: |
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st.subheader("🎈 Check & download results") |
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with c2: |
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CSVButton2 = download_button(preds, "sentiment-analysis-preds.csv", "📥 Download (.csv)") |
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st.header("") |
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df = pd.DataFrame(preds, columns=['text', 'label', 'score']) |
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import seaborn as sns |
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cmGreen = sns.light_palette("green", as_cmap=True) |
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cmRed = sns.light_palette("red", as_cmap=True) |
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df = df.style.background_gradient( |
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cmap=cmGreen, |
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subset=["score"], |
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) |
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st.table(df) |
