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BART_utils.py

@@ -83,8 +83,7 @@ def plot_mbti(result):
     x, y = result.values()
     x_type, y_type = result.keys()
 
-    ax.broken_barh([(start, x), (x, x + y)], [10, 9],
-                   facecolors=("#FFC5BF", "#D4F0F0"))
+    ax.broken_barh([(start, x), (x, x + y)], [10, 9], facecolors=("#FFC5BF", "#D4F0F0"))
     ax.set_ylim(5, 15)
     ax.set_xlim(0, 100)
     ax.spines["left"].set_visible(False)

LICENSE

@@ -0,0 +1,201 @@
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Pipfile

@@ -0,0 +1,18 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+matplotlib = "==3.5.1"
+matplotlib-inline = "==0.1.3"
+numpy = "*"
+streamlit = "==1.5.1"
+torch = "==1.10.2"
+watchdog = "*"
+transformers = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.8"

README.md

@@ -1,12 +1,53 @@
----
-title: MBTI_translator
-emoji: ⚡
-colorFrom: red
-colorTo: gray
-sdk: streamlit
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+# Text to MBTI (Zero Shot - model)
+😄 MBTI Translator (Infer you personality type based on your sentence)
+
+Using Streamlit library, this project is implementing application with UI of following project: https://github.com/ethHong/text_mbti
+
+## About the project & Examples
+* Pur the sentence and click 'generate' button. Using Zero shot classification model, the app predict the most probable MBTI of yourself. 
+<img width="708" alt="Screenshot 2023-01-08 at 3 04 29 PM" src="https://user-images.githubusercontent.com/43837843/211182872-e0db4da8-9c7e-48bd-a3ec-04165baf5c7e.png">
+
+<img width="697" alt="Screenshot 2023-01-08 at 3 05 18 PM" src="https://user-images.githubusercontent.com/43837843/211182926-cd88e370-4790-40f3-a7f1-a964d09366ef.png">
+
+
+## How it works?
+* This app uases  Zero-shot classification model from Facebook. 
+* The model uses pre-defined dictionary data which representes each of 16 personality type
+* It compute probability input keyword be relevant to each of the keywords mapped in dictionary of 16 personality types. 
+![image](https://user-images.githubusercontent.com/43837843/211183359-ad2cf761-99a7-467f-8bb9-cdf308bc019e.png)
+
+
+## Output sample
+```
+Input: "I stayed home all day"
+
+===
+
+Output:
+
+You are:  ISFP
+Ratio {'E': 27.338588094108168, 'I': 72.66141190589182} {'N': 22.149243913056992, 'S': 77.85075608694301} {'T': 46.17274433748438, 'F': 53.82725566251562} {'P': 57.30466611213056, 'J': 42.69533388786944}
+```
+
+```
+Input: "I'm making plans for my trip to Osaka. I'm so excited!"
+
+===
+
+Output:
+
+You are:  ESTJ
+Ratio {'E': 71.53464326345417, 'I': 28.46535673654582} {'N': 35.33135528913844, 'S': 64.66864471086156} {'T': 58.70273162646018, 'F': 41.29726837353982} {'P': 46.96476087995551, 'J': 53.03523912004449}
+```
+
+## Model and requirements
+* Model reference: https://huggingface.co/facebook/bart-large-mnli
+* Environment setup:
+> 02.20 update: Use requirements.txt to setup required libraries. 
+
+```
+pip install -r requirements.txt
+```
+```
+streamlit run app.py
+```

app.py

@@ -1,5 +1,12 @@
 import streamlit as st
-from BART_utils import get_prob, judge_mbti, compute_score, mbti_translator, plot_mbti, device
+from BART_utils import (
+    get_prob,
+    judge_mbti,
+    compute_score,
+    mbti_translator,
+    plot_mbti,
+    device,
+)
 
 
 st.title("MBTI translator")
@@ -12,19 +19,20 @@ st.subheader("Running on {}".format(device + processor))
 st.header("💻Infer my MBTI from my langauge (What I speak)")
 st.write("🤗Give any sentences: I'll try to guess your MBTI")
 st.header("🤔How it works??:")
-st.write("Using Zero-Shot NLI model, it computes probability of sentence and MBTI keywords")
+st.write(
+    "Using Zero-Shot NLI model, it computes probability of sentence and MBTI keywords"
+)
 st.write("More about the model: https://github.com/ethHong/mbti_translator_demo")
 
 user_input = st.text_input("👇👇Put your sentence here", "I stayed home all day")
-submit = st.button("Generate:")
+submit = st.button("Generate")
 
 if submit:
     with st.spinner("AI is analysing result..."):
-        output_mbti, output_ratio = mbti_translator(
-            user_input)
+        output_mbti, output_ratio = mbti_translator(user_input)
 
     st.success("Success")
     st.subheader("🤔Probable MBTI is...🎉 : " + output_mbti)
 
     for result in output_ratio:
-        plot_mbti(result)
+        plot_mbti(result)

main.ipynb

@@ -0,0 +1,72 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from BART_utils import get_prob, judge_mbti, compute_score, mbti_translator, device\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sukhy\\.virtualenvs\\mbti_translator_demo-C9tdDvXD\\lib\\site-packages\\transformers\\tokenization_utils_base.py:2284: FutureWarning: The `truncation_strategy` argument is deprecated and will be removed in a future version, use `truncation=True` to truncate examples to a max length. You can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to truncate to the maximal input size of the model (e.g. 512 for Bert).  If you have pairs of inputs, you can give a specific truncation strategy selected among `truncation='only_first'` (will only truncate the first sentence in the pairs) `truncation='only_second'` (will only truncate the second sentence in the pairs) or `truncation='longest_first'` (will iteratively remove tokens from the longest sentence in the pairs).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ISTP',\n",
+       " ({'E': 29.336015080590734, 'I': 70.66398491940927},\n",
+       "  {'N': 25.69581522631164, 'S': 74.30418477368836},\n",
+       "  {'T': 50.32619548200952, 'F': 49.67380451799048},\n",
+       "  {'P': 56.57599646556062, 'J': 43.42400353443938}))"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text=\"I Stayed home all day\"\n",
+    "mbti_translator(text)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "ca045e0e82b80c80cdacb54c8a532995176e76269958f022629c504a9c392a37"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.6 ('mbti_bart-OwFxlXtE')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -2,7 +2,6 @@ matplotlib==3.5.1
 matplotlib-inline==0.1.3
 numpy
 streamlit==1.5.1
+torch==1.10.2
 watchdog
-transformers
---extra-index-url https://download.pytorch.org/whl/cu113
-torch
+transformers