37hkbqf: Refactoring + Request log

app.py

@@ -1,107 +0,0 @@
-import gradio as gr
-import os
-
-import requests
-
-from spacy import displacy
-import streamlit as st
-
-os.system("python -m spacy download en_core_web_md")
-import spacy
-
-# entities group method
-# https://huggingface.co/spaces/crabz/sk-ner/blob/main/app.py
-
-options = {"ents": ["Observation",
-                    "Evaluation"],
-           "colors": {
-               "Observation": "#9bddff",
-               "Evaluation": "#f08080",
-           }
-
-           }
-
-nlp = spacy.load("en_core_web_md")
-
-
-def postprocess(classifications):
-    entities = []
-    for i in range(len(classifications)):
-        if classifications[i]['entity'] != 0:
-            if classifications[i]['entity'][0] == 'B':
-                j = i + 1
-                while j < len(classifications) and classifications[j]['entity'][0] == 'I':
-                    j += 1
-                entities.append((classifications[i]['entity'].split('-')[1], classifications[i]['start'],
-                                 classifications[j - 1]['end']))
-    while True:
-        merged = False
-        to_remove = []
-        merged_entities = []
-        for i in range(len(entities)):
-            for j in range(i + 1, len(entities)):
-                if entities[i] != entities[j] and entities[i][0] == entities[j][0] and \
-                        (entities[i][2] == entities[j][1] or entities[i][1] == entities[j][2]):
-                    to_remove.append(entities[i])
-                    to_remove.append(entities[j])
-
-                    new_start = min(entities[i][1], entities[j][1])
-                    new_end = max(entities[i][2], entities[j][2])
-                    merged_entities.append((entities[i][0], new_start, new_end))
-                    merged = True
-                    break
-            if merged:
-                break
-        for ent in to_remove:
-            entities.remove(ent)
-        entities += merged_entities
-        if not merged:
-            break
-    return entities
-
-
-def set_entities(sentence, entities):
-    doc = nlp(sentence)
-    ents = []
-    for label, start, end in entities:
-        ents.append(doc.char_span(start, end, label))
-    try:
-        doc.ents = ents
-    except TypeError:
-        pass
-    return doc
-
-
-def apply_ner(input_text_message: str):
-    auth_endpoint_token = st.secrets["auth_endpoint_token"]
-    endpoint_url = st.secrets["endpoint_url"]
-
-    headers = {
-        'Authorization': auth_endpoint_token,
-        'Content-Type': 'application/json',
-    }
-
-    json_data = {
-        'inputs': input_text_message,
-    }
-
-    response = requests.post(endpoint_url, headers=headers, json=json_data)
-
-    classifications = response.json()
-    entities = postprocess(classifications)
-    doc = set_entities(input_text_message, entities)
-    displacy_html = displacy.render(doc, style="ent", options=options)
-    return displacy_html
-
-
-examples = ['Things are complicated because we still live together but we have separate lives',
-            'My dad is a monster and took his anger out on my mom by verbally abusing her and when she left he eventually moved on to my brother',
-            'A two months ago, she was chatting with some random guy',
-            'Not I have a horrid relationship with my brother we’ve never gotten along and probably never will',
-            'I was outside trying to leave and he caught me to explain why Im so rude',
-            ]
-
-iface = gr.Interface(fn=apply_ner, inputs=gr.inputs.Textbox(lines=5, placeholder="Enter your text here",
-                                                            label='Check your text for compliance with the NVC rules'),
-                     outputs="html", examples=examples)
-iface.launch()

app/classificator.py

@@ -0,0 +1,78 @@
+from dataclasses import dataclass
+from typing import List, Dict, Any
+
+import requests
+
+
+class ClassificationError(Exception):
+    pass
+
+
+@dataclass
+class Classification:
+    entity: str
+    start: int
+    end: int
+
+    def dict(self) -> Dict[str, Any]:
+        return {
+            'entity': self.entity,
+            'start': self.start,
+            'end': self.end
+        }
+
+
+class Classificator:
+
+    def __init__(self, config: Dict[str, Any]):
+        """
+        Initialize the classificator with the given configuration
+        """
+        self._config = config
+
+    def classify(self, text: str) -> List[Any]:
+        raw_data = self.send_request(text)
+        return self.post_process(raw_data)
+
+    def send_request(self, text: str) -> List[Dict[str, Any]]:
+        """
+            Process the text and return a list of dictionaries with the following keys
+        """
+
+        headers = {
+            'Authorization': self._config['auth_endpoint_token'],
+            'Content-Type': 'application/json',
+        }
+        try:
+            response = requests.post(self._config['endpoint_url'], headers=headers, json={'inputs': text})
+            return response.json()
+        except Exception:
+            raise ClassificationError('Classification failed')
+
+    @staticmethod
+    def post_process(raw_data: List[Dict[str, Any]]) -> List[Classification]:
+        """
+            Process the raw data and return a list of dictionaries with the following keys
+
+            raw_data is a list of dictionaries with the following keys
+            {'entity': 'B-Evaluation', 'score': 0.86011535, 'index': 1, 'word': 'Things', 'start': 0, 'end': 6}
+
+            result is a list of classifications with the following keys
+            Classification(entity='Evaluation', start=0, end=6)
+        """
+        classifications = []
+
+        current_entity = None
+        for item in raw_data:
+            if current_entity is None or current_entity != item['entity'][2:]:
+                current_entity = item['entity'][2:]
+                classifications.append(
+                    Classification(
+                        entity=current_entity,
+                        start=item['start'],
+                        end=item['end']
+                    )
+                )
+            else:
+                classifications[-1].end = item['end']
+        return classifications

app/models/request.py

@@ -0,0 +1,15 @@
+from datetime import datetime
+
+from pony.orm import PrimaryKey, Required, Json
+
+from app.settings import db
+
+
+class Request(db.Entity):
+    id = PrimaryKey(int, auto=True)
+    hash = Required(str)
+    text = Required(str)
+    rating = Required(int, sql_default=0)
+    result = Required(Json)
+    created_at = Required(datetime, default=datetime.now)
+    updated_at = Required(datetime, default=datetime.now)

app/settings.py

@@ -0,0 +1,14 @@
+import streamlit as st
+from pony.orm import Database
+
+db = Database()
+db.bind(
+    provider='postgres',
+    user=st.secrets['pg_user'],
+    password=st.secrets['pg_password'],
+    host=st.secrets['pg_host'],
+    port=st.secrets['pg_port'],
+    database=st.secrets['pg_database']
+)
+AUTH_ENDPOINT_TOKEN = st.secrets["auth_endpoint_token"]
+ENDPOINT_URL = st.secrets["endpoint_url"]

application.py

@@ -0,0 +1,95 @@
+import hashlib
+import logging
+import os
+from datetime import datetime
+from typing import Dict, Any, List
+
+import gradio as gr
+import spacy
+from pony.orm import db_session
+from spacy import displacy
+
+from app.classificator import Classificator, Classification
+from app.models.request import Request
+from app.settings import AUTH_ENDPOINT_TOKEN, ENDPOINT_URL, db
+
+
+class Application:
+    examples = [
+        'Things are complicated because we still live together but we have separate lives',
+        'My dad is a monster and took his anger out on my mom by verbally abusing her and when she left he '
+        'eventually moved on to my brother',
+        'A two months ago, she was chatting with some random guy',
+        'Not I have a horrid relationship with my brother we’ve never gotten along and probably never will',
+        'I was outside trying to leave and he caught me to explain why Im so rude',
+    ]
+
+    def __init__(self, classificator: Classificator, options: Dict[str, Any]):
+        self.options = options
+        self.classificator = classificator
+        self.nlp = spacy.load("en_core_web_md")
+
+    def handle(self, input_text: str) -> str:
+        """
+            Handle the input text and return the result as rendered html
+        """
+        if input_text is None or input_text == '':
+            return ''
+
+        classifications = self.classificator.classify(input_text)
+        request = self.log_request(input_text, classifications)
+        # TODO: тут надо взять хеш или ид, прокинуть его для формирования кнопок с оценкой
+        return self.render(input_text, classifications)
+
+    @staticmethod
+    @db_session
+    def log_request(input_text: str, classifications: List[Classification]) -> Request:
+        """
+            Log the request to the database
+        """
+        return Request(
+            text=input_text,
+            hash=hashlib.md5(input_text.encode()).hexdigest(),
+            created_at=datetime.now(),
+            updated_at=datetime.now(),
+            rating=0,
+            result=[c.dict() for c in classifications]
+        )
+
+    def render(self, input_text: str, classifications: List[Classification]) -> str:
+        """
+            Render the input text and the classifications as html text with labels
+        """
+        document = self.nlp(input_text)
+        try:
+            document.ents = [
+                document.char_span(classification.start, classification.end, classification.entity) for
+                classification in classifications
+            ]
+        except Exception as exc:
+            logging.exception(exc)
+        return displacy.render(document, style="ent", options=self.options)
+
+    def run(self):
+        iface = gr.Interface(
+            fn=self.handle, inputs=gr.Textbox(
+                lines=5, placeholder="Enter your text here",
+                label='Check your text for compliance with the NVC rules'),
+            outputs=["html"], examples=self.examples
+        )
+        iface.launch()
+
+
+if __name__ == '__main__':
+    os.system("python -m spacy download en_core_web_md")
+    db.generate_mapping(create_tables=True)
+    application = Application(
+        classificator=Classificator(
+            config={
+                'auth_endpoint_token': AUTH_ENDPOINT_TOKEN,
+                'endpoint_url': ENDPOINT_URL
+            }
+        ),
+        options={"ents": ["Observation", "Evaluation"], "colors": {"Observation": "#9bddff", "Evaluation": "#f08080"}}
+    )
+    application.run()

poetry.lock

@@ -0,0 +1,1570 @@
+[[package]]
+name = "aiohttp"
+version = "3.8.3"
+description = "Async http client/server framework (asyncio)"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+aiosignal = ">=1.1.2"
+async-timeout = ">=4.0.0a3,<5.0"
+attrs = ">=17.3.0"
+charset-normalizer = ">=2.0,<3.0"
+frozenlist = ">=1.1.1"
+multidict = ">=4.5,<7.0"
+yarl = ">=1.0,<2.0"
+
+[package.extras]
+speedups = ["aiodns", "brotli", "cchardet"]
+
+[[package]]
+name = "aiosignal"
+version = "1.3.1"
+description = "aiosignal: a list of registered asynchronous callbacks"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+frozenlist = ">=1.1.0"
+
+[[package]]
+name = "altair"
+version = "4.2.0"
+description = "Altair: A declarative statistical visualization library for Python."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+entrypoints = "*"
+jinja2 = "*"
+jsonschema = ">=3.0"
+numpy = "*"
+pandas = ">=0.18"
+toolz = "*"
+
+[package.extras]
+dev = ["black", "docutils", "ipython", "flake8", "pytest", "sphinx", "mistune (<2.0.0)", "m2r", "vega-datasets", "recommonmark"]
+
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+version = "3.6.2"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+category = "main"
+optional = false
+python-versions = ">=3.6.2"
+
+[package.dependencies]
+idna = ">=2.8"
+sniffio = ">=1.1"
+
+[package.extras]
+doc = ["packaging", "sphinx-rtd-theme", "sphinx-autodoc-typehints (>=1.2.0)"]
+test = ["coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "contextlib2", "uvloop (<0.15)", "mock (>=4)", "uvloop (>=0.15)"]
+trio = ["trio (>=0.16,<0.22)"]
+
+[[package]]
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+description = "Timeout context manager for asyncio programs"
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+optional = false
+python-versions = ">=3.6"
+
+[[package]]
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+description = "Classes Without Boilerplate"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[package.extras]
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+docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
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+tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"]
+
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+
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+
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+
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+test-minimal = ["pytest"]
+test-no-codebase = ["pytest", "matplotlib", "pillow"]
+
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+ssh = ["bcrypt (>=3.1.5)"]
+test = ["pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,!=3.79.2)"]
+
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+
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+
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pyproject.toml

@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "token-classification"
+version = "0.1.0"
+description = ""
+authors = ["Empy Team"]
+
+[tool.poetry.dependencies]
+python = "^3.10"
+gradio = "^3.12.0"
+requests = "^2.28.1"
+spacy = "^3.4.3"
+streamlit = "^1.15.2"
+pony = "^0.7.16"
+psycopg2 = "^2.9.5"
+
+[tool.poetry.dev-dependencies]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

requirements.txt

@@ -1,3 +0,0 @@
-gradio==3.11.0
-requests==2.28.1
-spacy==3.4.3