Feature/google api

notebooks/submission_endpoint_interations.ipynb

@@ -0,0 +1,212 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python_defaultSpec_1600367979214",
+   "display_name": "Python 3.7.6 64-bit ('ds-base': conda)"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# outlining the submission endpoints interactions\n",
+    "\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from requests import get, post"
+   ]
+  },
+  {
+   "source": [
+    "## Launch the API via uvicorn using the following command:\n",
+    "```\n",
+    "export GOOGLE_CREDS=[content of service account key file]\n",
+    "uvicorn app.main:app --reload --workers 1 --host 0.0.0.0 --port 8000\n",
+    "```"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## the following cell will check if the api is runnning and accepting requests"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "200"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "server_check = get(\"http://0.0.0.0:8000\")\n",
+    "server_check.status_code"
+   ]
+  },
+  {
+   "source": [
+    "## Open a local file and nest it in a files dictonary"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "{'files': ('3101.jpg', <_io.BufferedReader name='3101.jpg'>)}"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "image = open('3101.jpg', 'rb')\n",
+    "files = {\"files\":(image.name, image)}\n",
+    "files"
+   ]
+  },
+  {
+   "source": [
+    "## Make a post request to the `/submission/text` enpoint using the keyword `files` and the url argument `story_id`"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "200\n"
+    }
+   ],
+   "source": [
+    "story_id = \"3101\"\n",
+    "args = f\"?story_id={story_id}\"\n",
+    "response = post(f\"http://0.0.0.0:8000/submission/text\" + args, files=files)\n",
+    "print(response.status_code)\n"
+   ]
+  },
+  {
+   "source": [
+    "## Open an illustraion file"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "{'files': ('questionable.png', <_io.BufferedReader name='questionable.png'>)}"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "image = open('questionable.png', 'rb')\n",
+    "files = {\"files\":(image.name, image)}\n",
+    "files"
+   ]
+  },
+  {
+   "source": [
+    "## post to the `/submission/illustration` enpoint and print the results"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "200\n"
+    }
+   ],
+   "source": [
+    "response = post(\"http://0.0.0.0:8000/submission/illustration\", files=files)\n",
+    "print(response.status_code)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "b'{\"is_flagged\":true,\"reason\":[\"adult: VERY_UNLIKELY\",\"violence: VERY_UNLIKELY\",\"racy: POSSIBLE\"]}'"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "response.content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}

project/app/api/submission.py

@@ -1,17 +1,21 @@
 import logging
 from fastapi import APIRouter, File, UploadFile
 from pydantic import BaseModel, Field, validator
+from app.utils.google_api import GoogleAPI, NoTextFoundException
+import json
 
 router = APIRouter()
 log = logging.getLogger(__name__)
+# init the GoogleAPI service to fetch responses from the google vision api
+vision = GoogleAPI()
 
 
 class Submission(BaseModel):
     """Data Model to parse Request body JSON, using the Default values
     will check if the s3 API server is currently accepting requests"""
 
     story_id: str = Field(..., example="12345")
-    story_file: bytes = Field(..., example=b"10gh1r447/s4413.")
+    story_file: str = Field(..., example=b"10gh1r447/s4413.")
 
     @validator("story_id")
     def no_none_ids(cls, value):
@@ -28,7 +32,7 @@ def check_file(cls, value):
 
 
 @router.post("/submission/text")
-async def submission_text(sub: Submission):
+async def submission_text(story_id: str, files: UploadFile = File(...)):
     """This function takes the passed file in Submission and calls two services,
     one that uses the Google Vision API and transcribes the text from the file
     and looks for moderation markers. the other service will take the binary
@@ -39,30 +43,48 @@ async def submission_text(sub: Submission):
 
     ## Arguments:
     -----------
-
-    sub {Submission} - submission model that is created on post
+    story_id `str` - story_id
+    story_file `UploadFile` - UGC passed with enctype=multipart/form-data
 
     ## Returns:
     -----------
 
-    json - {"is_flagged": bool, "s3_link": type(url)}
+    response `json` - {"is_flagged": bool, "s3_link": type(url)}
     """
-    return {sub}
+    # catch custom exception for no text
+    try:
+        # await for the vision API to process the image
+        transcript = await vision.transcribe(files)
+        story_id = story_id
+
+    # log the error then return what the error is
+    except NoTextFoundException as e:
+        log.error(e, stack_info=True)
+        return {"error": e}
+    print((story_id, transcript))
+
+    # return moderation flag and s3_link for that file (not yet implemented)
+    return {"is_flagged": None, "s3_path": None}
 
 
 @router.post("/submission/illustration")
-async def submission_illustration(sub: Submission):
-    """Function that takes an illustration from form data and checks the
-    illustration against the google.cloud.vision api and see's if the content
-    is NSFW
+async def submission_illustration(files: UploadFile = File(...)):
+    """Function that checks the illustration against the google.cloud.vision
+    api and see's if the content is NSFW.
 
     ## Arguments:
     -----------
+    files `UploadFile` - UGC to be uploaded, transcribed, and stored
 
-    sub {Submission} - submission model that is created on post
+    eg.
+    ```python3
+    files = {"files": (file.name, file)}
+    ```
 
     ## Returns:
     -----------
 
-    json - {"is_flagged": bool, "s3_link": type(url)}"""
-    return {sub}
+    response `json` - {"is_flagged": bool, "reason":`reason`}"""
+    response = await vision.detect_safe_search(files)
+    return response
+