application files

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+from transformers import pipeline
+
+
+article = '''<img src="https://corporateweb-v3-corporatewebv3damstrawebassetbuck-1lruglqypgb84.s3-ap-southeast-2.amazonaws.com/public/cta-2.jpg"/> '''
+
+examples = [
+  [
+'''
+A truck narrowly missed a person on a bicycle when they were reversing out of the depot on Friday.  \
+    It was early morning before the sun was up and the cyclist did not have a light.  Fortunately the \
+        driver spotted the rider and braked heavily to avoid a collision.
+'''],
+  [
+'''
+When making a coffee I noticed the cord to the coffee machine was frayed and tagged it out of service.  Now I need to find a barista!'''],
+  [
+'''
+A worker was using a grinder in a confined space when he became dizzy from the fumes in the area and had to be helped out.  \
+The gas monitor he was using was found to be faulty and when the area was assessed with another monitor there was an \
+unacceptably high level of CO2 in the area''']]
+
+
+title = "Incident Prioritisation Tool"
+description = "Triage new incidents based on a distilbert-uncased NLP model that has been fine tuned on descriptions of incidents \
+    that have been risk rated in the past"
+
+pipe = pipeline("text-classification", model="mrosinski/autotrain-distilbert-risk-ranker-1593356256")
+
+def predict(text):
+    # if len(text[0]) > 60:
+    preds = pipe(text)[0]
+    return preds["label"].title(), f'Confidence Score: {round(preds["score"]*100, 1)}%'
+    # else:
+    #     return 'Invalid entry', 'Try adding more information to describe the incident'
+
+  
+gradio_ui = gr.Interface(
+    fn=predict,         
+    title=title,
+    description=description,
+    inputs=[
+        gr.inputs.Textbox(lines=5, label="Paste some text here"),
+    ],
+    outputs=[
+        gr.outputs.Textbox(label="Label"),
+        gr.outputs.Textbox(label="Score"),
+    ],
+    examples=examples, 
+    article=article 
+    
+)
+
+gradio_ui.launch(debug=True)
+

test-model.ipynb

@@ -0,0 +1,289 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/mattrosinski/git/transformers-demos\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/mattrosinski/mambaforge/bin/python\n"
+     ]
+    }
+   ],
+   "source": [
+    "!which python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "from transformers import pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipe = pipeline(\"text-classification\", model=\"mrosinski/autotrain-distilbert-risk-ranker-1593356256\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = ['''\n",
+    "A truck narrowly missed a person on a bicycle when they were reversing out of the depot on Friday.  \\\n",
+    "    It was early morning before the sun was up and the cyclist did not have a light.  Fortunately the \\\n",
+    "        driver spotted the rider and braked heavily to avoid a collision.\n",
+    "''']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "60"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len('A truck narrowly missed a person on a bicycle when they were')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "277"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(text[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'label': 'high risk', 'score': 0.7180770635604858}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds = pipe(text)[0]\n",
+    "preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7180770635604858"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score = preds['score']\n",
+    "score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "float"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('High Risk', 'Confidence score: 71.8%')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds[\"label\"].title(), f'Confidence Score: {round(preds[\"score\"], 3)*100}%'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def predict(text):\n",
+    "    if len(text) < 60:\n",
+    "        return 'Invalid entry', 'Try adding more information to describe the incident'\n",
+    "    preds = pipe(text)[0]\n",
+    "    return preds[\"label\"].title(), f'Confidence Score: {round(preds[\"score\"], 3)*100}%'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "string = 'some text'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict(string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.10",
+   "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": "3.9.10"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "b29df53a373a75f04ac216b720f486bfd73e41a5a0018838dedd490de94cf09c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}