added flask for python api

ml/foodClassifier.py

@@ -1,34 +1,40 @@
-"""
-Function must have `huggingfaceToken.json` to get access to ml model
-- this is basically an async function that returns the predicted label of the
-food image that is inputted. 
-- images must be located physically
-"""
+from flask import Flask, request
+
+app = Flask(__name__)
 
 import aiohttp
 import json
 import asyncio
 
-token = json.load(open("ml/huggingfaceToken.json"))['token']
+token = json.load(open("ml/huggingfaceToken.json"))["token"]
 
 API_URL = "https://api-inference.huggingface.co/models/nateraw/food"
 headers = {"Authorization": f"Bearer {token}"}
 
 
+@app.route("/predict/<filename>", methods=["GET"])
 async def query(filename):
-    with open(filename, "rb") as f:
-        data = f.read()
+    try:
+        with open(filename, "rb") as f:
+            data = f.read()
+
+        async with aiohttp.ClientSession() as session:
+            async with session.post(API_URL, headers=headers, data=data) as response:
+                return await response.json()
 
-    async with aiohttp.ClientSession() as session:
-        async with session.post(API_URL, headers=headers, data=data) as response:
-            return await response.json()
+    except FileNotFoundError:
+        return "File not found", 404
 
 
 async def main():
+    # This part is used for testing the function separately.
     output = await query("ml/pizza.webp")
     print(output)
 
 
 if __name__ == "__main__":
     loop = asyncio.get_event_loop()
     loop.run_until_complete(main())
+
+if __name__ == "__main__":
+    app.run()

ml/foodClassifier.ts

@@ -31,4 +31,4 @@ const main = async (): Promise<void> => {
   }
 };
 
-main();
+main();

ml/hello.py

@@ -0,0 +1,5 @@
+from flask import Flask
+app = Flask(__name__)
+@app.route('/')
+def hello_world():
+    return 'Hello world!'