├── requirements.txt ├── .streamlit └── config.toml ├── images ├── logo.png ├── HFlogo.png └── SF_logo_icon.png ├── dashboard_utils ├── .DS_Store ├── __pycache__ │ ├── gui.cpython-310.pyc │ └── gui.cpython-37.pyc └── gui.py ├── README.md ├── LICENSE └── streamlit_app.py /requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | streamlit-tags 3 | -------------------------------------------------------------------------------- /.streamlit/config.toml: -------------------------------------------------------------------------------- 1 | [theme] 2 | base="light" 3 | primaryColor="#29B4E8" -------------------------------------------------------------------------------- /images/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/streamlit/example-app-zero-shot-text-classifier/HEAD/images/logo.png -------------------------------------------------------------------------------- /images/HFlogo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/streamlit/example-app-zero-shot-text-classifier/HEAD/images/HFlogo.png -------------------------------------------------------------------------------- /dashboard_utils/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/streamlit/example-app-zero-shot-text-classifier/HEAD/dashboard_utils/.DS_Store -------------------------------------------------------------------------------- /images/SF_logo_icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/streamlit/example-app-zero-shot-text-classifier/HEAD/images/SF_logo_icon.png -------------------------------------------------------------------------------- /dashboard_utils/__pycache__/gui.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/streamlit/example-app-zero-shot-text-classifier/HEAD/dashboard_utils/__pycache__/gui.cpython-310.pyc -------------------------------------------------------------------------------- /dashboard_utils/__pycache__/gui.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/streamlit/example-app-zero-shot-text-classifier/HEAD/dashboard_utils/__pycache__/gui.cpython-37.pyc -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # 🤗 Zero-shot Text Classifier 3 | 4 | [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://zero-shot-text-classifier.streamlitapp.com/) 5 | 6 | Classify keyphrases fast and on-the-fly with this mighty app. No ML training needed! Create classifying labels, paste your keyphrases, and you're off! 🚀 7 | 8 | You can set these labels anything, e.g.: 9 | - `Positive`, `Negative` and `Neutral` for sentiment analysis 10 | - `Angry`, `Happy`, `Emotional` for emotion analysis 11 | - `Navigational`, `Transactional`, Informational for intent classification purposes 12 | - Your product range (`Bags`, `Shoes`, `Boots` etc.) 13 | 14 | You decide! 15 | 16 | ### About the app 17 | 18 | - App created by [Datachaz](https://twitter.com/DataChaz) using 🎈[Streamlit](https://streamlit.io/) and [HuggingFace](https://huggingface.co/inference-api)'s [Distilbart-mnli-12-3](https://huggingface.co/valhalla/distilbart-mnli-12-3) model. 19 | - Deployed on [Streamlit Cloud](https://streamlit.io/cloud) ☁️ 20 | 21 | 22 | 23 | 24 | ### Questions? Comments? 25 | 26 | Please ask in the [Streamlit community](https://discuss.streamlit.io). 27 | -------------------------------------------------------------------------------- /dashboard_utils/gui.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | 3 | # Import for keyboard shortcuts 4 | import streamlit.components.v1 as components 5 | 6 | def load_keyboard_class(): 7 | """This class enables to render some elements as if they were . 8 | Without this class, currently looks the same as in Streamlit. 9 | Usage: 10 | load_keyboard_class() 11 | st.write(' Press here ', unsafe_allow_html=True) 12 | """ 13 | st.write( 14 | """""", 29 | unsafe_allow_html=True, 30 | ) 31 | 32 | 33 | 34 | 35 | def keyboard_to_url( 36 | key: str = None, 37 | key_code: int = None, 38 | url: str = None, 39 | ): 40 | """Map a keyboard key to open a new tab with a given URL. 41 | Args: 42 | key (str, optional): Key to trigger (example 'k'). Defaults to None. 43 | key_code (int, optional): If key doesn't work, try hard-coding the key_code instead. Defaults to None. 44 | url (str, optional): Opens the input URL in new tab. Defaults to None. 45 | """ 46 | 47 | assert not ( 48 | key and key_code 49 | ), """You can not provide key and key_code. 50 | Either give key and we'll try to find its associated key_code. Or directly 51 | provide the key_code.""" 52 | 53 | assert (key or key_code) and url, """You must provide key or key_code, and a URL""" 54 | 55 | if key: 56 | key_code_js_row = f"const keyCode = '{key}'.toUpperCase().charCodeAt(0);" 57 | if key_code: 58 | key_code_js_row = f"const keyCode = {key_code};" 59 | 60 | components.html( 61 | f""" 62 | 79 | """, 80 | height=0, 81 | width=0, 82 | ) -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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IMPORTING LIBRARIES ############ 2 | 3 | # Import streamlit, requests for API calls, and pandas and numpy for data manipulation 4 | 5 | import streamlit as st 6 | import requests 7 | import pandas as pd 8 | import numpy as np 9 | from streamlit_tags import st_tags # to add labels on the fly! 10 | 11 | 12 | ############ 2. SETTING UP THE PAGE LAYOUT AND TITLE ############ 13 | 14 | # `st.set_page_config` is used to display the default layout width, the title of the app, and the emoticon in the browser tab. 15 | 16 | st.set_page_config( 17 | layout="centered", page_title="Zero-Shot Text Classifier", page_icon="❄️" 18 | ) 19 | 20 | ############ CREATE THE LOGO AND HEADING ############ 21 | 22 | # We create a set of columns to display the logo and the heading next to each other. 23 | 24 | 25 | c1, c2 = st.columns([0.32, 2]) 26 | 27 | # The snowflake logo will be displayed in the first column, on the left. 28 | 29 | with c1: 30 | 31 | st.image( 32 | "images/logo.png", 33 | width=85, 34 | ) 35 | 36 | 37 | # The heading will be on the right. 38 | 39 | with c2: 40 | 41 | st.caption("") 42 | st.title("Zero-Shot Text Classifier") 43 | 44 | 45 | # We need to set up session state via st.session_state so that app interactions don't reset the app. 46 | 47 | if not "valid_inputs_received" in st.session_state: 48 | st.session_state["valid_inputs_received"] = False 49 | 50 | 51 | ############ SIDEBAR CONTENT ############ 52 | 53 | st.sidebar.write("") 54 | 55 | # For elements to be displayed in the sidebar, we need to add the sidebar element in the widget. 56 | 57 | # We create a text input field for users to enter their API key. 58 | 59 | API_KEY = st.sidebar.text_input( 60 | "Enter your HuggingFace API key", 61 | help="Once you created you HuggingFace account, you can get your free API token in your settings page: https://huggingface.co/settings/tokens", 62 | type="password", 63 | ) 64 | 65 | # Adding the HuggingFace API inference URL. 66 | API_URL = "https://api-inference.huggingface.co/models/valhalla/distilbart-mnli-12-3" 67 | 68 | # Now, let's create a Python dictionary to store the API headers. 69 | headers = {"Authorization": f"Bearer {API_KEY}"} 70 | 71 | 72 | st.sidebar.markdown("---") 73 | 74 | 75 | # Let's add some info about the app to the sidebar. 76 | 77 | st.sidebar.write( 78 | """ 79 | 80 | App created by [Charly Wargnier](https://twitter.com/DataChaz) using [Streamlit](https://streamlit.io/)🎈 and [HuggingFace](https://huggingface.co/inference-api)'s [Distilbart-mnli-12-3](https://huggingface.co/valhalla/distilbart-mnli-12-3) model. 81 | 82 | """ 83 | ) 84 | 85 | 86 | ############ TABBED NAVIGATION ############ 87 | 88 | # First, we're going to create a tabbed navigation for the app via st.tabs() 89 | # tabInfo displays info about the app. 90 | # tabMain displays the main app. 91 | 92 | MainTab, InfoTab = st.tabs(["Main", "Info"]) 93 | 94 | with InfoTab: 95 | 96 | st.subheader("What is Streamlit?") 97 | st.markdown( 98 | "[Streamlit](https://streamlit.io) is a Python library that allows the creation of interactive, data-driven web applications in Python." 99 | ) 100 | 101 | st.subheader("Resources") 102 | st.markdown( 103 | """ 104 | - [Streamlit Documentation](https://docs.streamlit.io/) 105 | - [Cheat sheet](https://docs.streamlit.io/library/cheatsheet) 106 | - [Book](https://www.amazon.com/dp/180056550X) (Getting Started with Streamlit for Data Science) 107 | """ 108 | ) 109 | 110 | st.subheader("Deploy") 111 | st.markdown( 112 | "You can quickly deploy Streamlit apps using [Streamlit Community Cloud](https://streamlit.io/cloud) in just a few clicks." 113 | ) 114 | 115 | 116 | with MainTab: 117 | 118 | # Then, we create a intro text for the app, which we wrap in a st.markdown() widget. 119 | 120 | st.write("") 121 | st.markdown( 122 | """ 123 | 124 | Classify keyphrases on the fly with this mighty app. No training needed! 125 | 126 | """ 127 | ) 128 | 129 | st.write("") 130 | 131 | # Now, we create a form via `st.form` to collect the user inputs. 132 | 133 | # All widget values will be sent to Streamlit in batch. 134 | # It makes the app faster! 135 | 136 | with st.form(key="my_form"): 137 | 138 | ############ ST TAGS ############ 139 | 140 | # We initialize the st_tags component with default "labels" 141 | 142 | # Here, we want to classify the text into one of the following user intents: 143 | # Transactional 144 | # Informational 145 | # Navigational 146 | 147 | labels_from_st_tags = st_tags( 148 | value=["Transactional", "Informational", "Navigational"], 149 | maxtags=3, 150 | suggestions=["Transactional", "Informational", "Navigational"], 151 | label="", 152 | ) 153 | 154 | # The block of code below is to display some text samples to classify. 155 | # This can of course be replaced with your own text samples. 156 | 157 | # MAX_KEY_PHRASES is a variable that controls the number of phrases that can be pasted: 158 | # The default in this app is 50 phrases. This can be changed to any number you like. 159 | 160 | MAX_KEY_PHRASES = 50 161 | 162 | new_line = "\n" 163 | 164 | pre_defined_keyphrases = [ 165 | "I want to buy something", 166 | "We have a question about a product", 167 | "I want a refund through the Google Play store", 168 | "Can I have a discount, please", 169 | "Can I have the link to the product page?", 170 | ] 171 | 172 | # Python list comprehension to create a string from the list of keyphrases. 173 | keyphrases_string = f"{new_line.join(map(str, pre_defined_keyphrases))}" 174 | 175 | # The block of code below displays a text area 176 | # So users can paste their phrases to classify 177 | 178 | text = st.text_area( 179 | # Instructions 180 | "Enter keyphrases to classify", 181 | # 'sample' variable that contains our keyphrases. 182 | keyphrases_string, 183 | # The height 184 | height=200, 185 | # The tooltip displayed when the user hovers over the text area. 186 | help="At least two keyphrases for the classifier to work, one per line, " 187 | + str(MAX_KEY_PHRASES) 188 | + " keyphrases max in 'unlocked mode'. You can tweak 'MAX_KEY_PHRASES' in the code to change this", 189 | key="1", 190 | ) 191 | 192 | # The block of code below: 193 | 194 | # 1. Converts the data st.text_area into a Python list. 195 | # 2. It also removes duplicates and empty lines. 196 | # 3. Raises an error if the user has entered more lines than in MAX_KEY_PHRASES. 197 | 198 | text = text.split("\n") # Converts the pasted text to a Python list 199 | linesList = [] # Creates an empty list 200 | for x in text: 201 | linesList.append(x) # Adds each line to the list 202 | linesList = list(dict.fromkeys(linesList)) # Removes dupes 203 | linesList = list(filter(None, linesList)) # Removes empty lines 204 | 205 | if len(linesList) > MAX_KEY_PHRASES: 206 | st.info( 207 | f"❄️ Note that only the first " 208 | + str(MAX_KEY_PHRASES) 209 | + " keyphrases will be reviewed to preserve performance. Fork the repo and tweak 'MAX_KEY_PHRASES' in the code to increase that limit." 210 | ) 211 | 212 | linesList = linesList[:MAX_KEY_PHRASES] 213 | 214 | submit_button = st.form_submit_button(label="Submit") 215 | 216 | ############ CONDITIONAL STATEMENTS ############ 217 | 218 | # Now, let us add conditional statements to check if users have entered valid inputs. 219 | # E.g. If the user has pressed the 'submit button without text, without labels, and with only one label etc. 220 | # The app will display a warning message. 221 | 222 | if not submit_button and not st.session_state.valid_inputs_received: 223 | st.stop() 224 | 225 | elif submit_button and not text: 226 | st.warning("❄️ There is no keyphrases to classify") 227 | st.session_state.valid_inputs_received = False 228 | st.stop() 229 | 230 | elif submit_button and not labels_from_st_tags: 231 | st.warning("❄️ You have not added any labels, please add some! ") 232 | st.session_state.valid_inputs_received = False 233 | st.stop() 234 | 235 | elif submit_button and len(labels_from_st_tags) == 1: 236 | st.warning("❄️ Please make sure to add at least two labels for classification") 237 | st.session_state.valid_inputs_received = False 238 | st.stop() 239 | 240 | elif submit_button or st.session_state.valid_inputs_received: 241 | 242 | if submit_button: 243 | 244 | # The block of code below if for our session state. 245 | # This is used to store the user's inputs so that they can be used later in the app. 246 | 247 | st.session_state.valid_inputs_received = True 248 | 249 | ############ MAKING THE API CALL ############ 250 | 251 | # First, we create a Python function to construct the API call. 252 | 253 | def query(payload): 254 | response = requests.post(API_URL, headers=headers, json=payload) 255 | return response.json() 256 | 257 | # The function will send an HTTP POST request to the API endpoint. 258 | # This function has one argument: the payload 259 | # The payload is the data we want to send to HugggingFace when we make an API request 260 | 261 | # We create a list to store the outputs of the API call 262 | 263 | list_for_api_output = [] 264 | 265 | # We create a 'for loop' that iterates through each keyphrase 266 | # An API call will be made every time, for each keyphrase 267 | 268 | # The payload is composed of: 269 | # 1. the keyphrase 270 | # 2. the labels 271 | # 3. the 'wait_for_model' parameter set to "True", to avoid timeouts! 272 | 273 | for row in linesList: 274 | api_json_output = query( 275 | { 276 | "inputs": row, 277 | "parameters": {"candidate_labels": labels_from_st_tags}, 278 | "options": {"wait_for_model": True}, 279 | } 280 | ) 281 | 282 | # Let's have a look at the output of the API call 283 | # st.write(api_json_output) 284 | 285 | # All the results are appended to the empty list we created earlier 286 | list_for_api_output.append(api_json_output) 287 | 288 | # then we'll convert the list to a dataframe 289 | df = pd.DataFrame.from_dict(list_for_api_output) 290 | 291 | st.success("✅ Done!") 292 | 293 | st.caption("") 294 | st.markdown("### Check the results!") 295 | st.caption("") 296 | 297 | # st.write(df) 298 | 299 | ############ DATA WRANGLING ON THE RESULTS ############ 300 | # Various data wrangling to get the data in the right format! 301 | 302 | # List comprehension to convert the score from decimals to percentages 303 | f = [[f"{x:.2%}" for x in row] for row in df["scores"]] 304 | 305 | # Join the classification scores to the dataframe 306 | df["classification scores"] = f 307 | 308 | # Rename the column 'sequence' to 'keyphrase' 309 | df.rename(columns={"sequence": "keyphrase"}, inplace=True) 310 | 311 | # The API returns a list of all labels sorted by score. We only want the top label. 312 | 313 | # For that, we need to select the first element in the 'labels' and 'classification scores' lists 314 | df["label"] = df["labels"].str[0] 315 | df["accuracy"] = df["classification scores"].str[0] 316 | 317 | # Drop the columns we don't need 318 | df.drop(["scores", "labels", "classification scores"], inplace=True, axis=1) 319 | 320 | # st.write(df) 321 | 322 | # We need to change the index. Index starts at 0, so we make it start at 1 323 | df.index = np.arange(1, len(df) + 1) 324 | 325 | # Display the dataframe 326 | st.write(df) 327 | 328 | cs, c1 = st.columns([2, 2]) 329 | 330 | 331 | 332 | 333 | # The code below is for the download button 334 | # Cache the conversion to prevent computation on every rerun 335 | 336 | with cs: 337 | 338 | @st.experimental_memo 339 | def convert_df(df): 340 | return df.to_csv().encode("utf-8") 341 | 342 | csv = convert_df(df) 343 | 344 | st.caption("") 345 | 346 | st.download_button( 347 | label="Download results", 348 | data=csv, 349 | file_name="classification_results.csv", 350 | mime="text/csv", 351 | ) 352 | 353 | 354 | --------------------------------------------------------------------------------