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README.md

@@ -1,15 +1 @@
-# Starter for deploying [fast.ai](https://www.fast.ai) models on [Render](https://render.com)
 
-This repo can be used as a starting point to deploy [fast.ai](https://github.com/fastai/fastai) models on Render.
-
-The sample app described here is up at https://fastai-v3.onrender.com. Test it out with bear images!
-
-You can test your changes locally by installing Docker and using the following command:
-
-```
-docker build -t fastai-v3 . && docker run --rm -it -p 5000:5000 fastai-v3
-```
-
-The guide for production deployment to Render is at https://course.fast.ai/deployment_render.html.
-
-Please use [Render's fast.ai forum thread](https://forums.fast.ai/t/deployment-platform-render/33953) for questions and support.

app/server.py

@@ -1,27 +1,60 @@
 import aiohttp
 import asyncio
 import uvicorn
-from fastai import *
 from fastai.vision import *
 from io import BytesIO
 from starlette.applications import Starlette
 from starlette.middleware.cors import CORSMiddleware
 from starlette.responses import HTMLResponse, JSONResponse
 from starlette.staticfiles import StaticFiles
+from PIL import Image as IM
 
-export_file_url = 'https://www.dropbox.com/s/6bgq8t6yextloqp/export.pkl?raw=1'
+##rollback
+##try
+export_file_url = 'https://drive.google.com/uc?export=download&id=1QVxKqLSZwYS42hEoVYztp1awvZX44ppC' #https://drive.google.com/u/0/uc?export=download&confirm=W7Y1&id=1BSva5kuYeZVnsE8M_kwO0QSILdFIgbQC
 export_file_name = 'export.pkl'
+export_file_name_2 = 'model.sav'
 
-classes = ['black', 'grizzly', 'teddys']
+classes = ['NORMAL', 'PNEUMONIA']
+
+"""
+['AFRICAN FIREFINCH', 'ALBATROSS', 'ALEXANDRINE PARAKEET', 'AMERICAN AVOCET', 'AMERICAN BITTERN', 'AMERICAN COOT', 'AMERICAN GOLDFINCH',
+           'AMERICAN KESTREL', 'AMERICAN PIPIT', 'AMERICAN REDSTART', 'ANHINGA', 'ANNAS HUMMINGBIRD', 'ANTBIRD', 'ARARIPE MANAKIN', 'ASIAN CRESTED IBIS',
+           'BALD EAGLE', 'BALI STARLING', 'BALTIMORE ORIOLE', 'BANANAQUIT', 'BAR-TAILED GODWIT', 'BARN OWL', 'BARN SWALLOW', 'BARRED PUFFBIRD',
+           'BAY-BREASTED WARBLER', 'BEARDED BARBET', 'BELTED KINGFISHER', 'BIRD OF PARADISE', 'BLACK FRANCOLIN', 'BLACK SKIMMER', 'BLACK SWAN',
+           'BLACK THROATED WARBLER', 'BLACK VULTURE', 'BLACK-CAPPED CHICKADEE', 'BLACK-NECKED GREBE', 'BLACK-THROATED SPARROW', 'BLACKBURNIAM WARBLER',
+           'BLUE GROUSE', 'BLUE HERON', 'BOBOLINK', 'BROWN NOODY', 'BROWN THRASHER', 'CACTUS WREN', 'CALIFORNIA CONDOR', 'CALIFORNIA GULL', 'CALIFORNIA QUAIL',
+           'CANARY', 'CAPE MAY WARBLER', 'CAPUCHINBIRD', 'CARMINE BEE-EATER', 'CASPIAN TERN', 'CASSOWARY', 'CHARA DE COLLAR', 'CHIPPING SPARROW', 'CHUKAR PARTRIDGE',
+           'CINNAMON TEAL', 'COCK OF THE  ROCK', 'COCKATOO', 'COMMON GRACKLE', 'COMMON HOUSE MARTIN', 'COMMON LOON', 'COMMON POORWILL', 'COMMON STARLING',
+           'COUCHS KINGBIRD', 'CRESTED AUKLET', 'CRESTED CARACARA', 'CROW', 'CROWNED PIGEON', 'CUBAN TODY', 'CURL CRESTED ARACURI', 'D-ARNAUDS BARBET',
+           'DARK EYED JUNCO', 'DOWNY WOODPECKER', 'EASTERN BLUEBIRD', 'EASTERN MEADOWLARK', 'EASTERN ROSELLA', 'EASTERN TOWEE', 'ELEGANT TROGON',
+           'ELLIOTS  PHEASANT', 'EMPEROR PENGUIN', 'EMU', 'EURASIAN MAGPIE', 'EVENING GROSBEAK', 'FLAME TANAGER', 'FLAMINGO', 'FRIGATE', 'GAMBELS QUAIL',
+           'GILA WOODPECKER', 'GILDED FLICKER', 'GLOSSY IBIS', 'GOLD WING WARBLER', 'GOLDEN CHEEKED WARBLER', 'GOLDEN CHLOROPHONIA', 'GOLDEN EAGLE',
+           'GOLDEN PHEASANT', 'GOLDEN PIPIT', 'GOULDIAN FINCH', 'GRAY CATBIRD', 'GRAY PARTRIDGE', 'GREEN JAY', 'GREY PLOVER', 'GUINEAFOWL', 'GYRFALCON',
+           'HARPY EAGLE', 'HAWAIIAN GOOSE', 'HOODED MERGANSER', 'HOOPOES', 'HORNBILL', 'HORNED GUAN', 'HORNED SUNGEM', 'HOUSE FINCH', 'HOUSE SPARROW',
+           'IMPERIAL SHAQ', 'INCA TERN', 'INDIAN BUSTARD', 'INDIGO BUNTING', 'JABIRU', 'JAVAN MAGPIE', 'KAKAPO', 'KILLDEAR', 'KING VULTURE', 'KIWI',
+           'KOOKABURRA', 'LARK BUNTING', 'LEARS MACAW', 'LILAC ROLLER', 'LONG-EARED OWL', 'MALABAR HORNBILL', 'MALACHITE KINGFISHER', 'MALEO', 'MALLARD DUCK',
+           'MANDRIN DUCK', 'MARABOU STORK', 'MASKED BOOBY', 'MIKADO  PHEASANT', 'MOURNING DOVE', 'MYNA', 'NICOBAR PIGEON', 'NORTHERN CARDINAL',
+           'NORTHERN FLICKER', 'NORTHERN GANNET', 'NORTHERN GOSHAWK', 'NORTHERN JACANA', 'NORTHERN MOCKINGBIRD', 'NORTHERN PARULA', 'NORTHERN RED BISHOP',
+           'OCELLATED TURKEY', 'OKINAWA RAIL', 'OSPREY', 'OSTRICH', 'PAINTED BUNTIG', 'PALILA', 'PARADISE TANAGER', 'PARUS MAJOR', 'PEACOCK', 'PELICAN',
+           'PEREGRINE FALCON', 'PHILIPPINE EAGLE', 'PINK ROBIN', 'PUFFIN', 'PURPLE FINCH', 'PURPLE GALLINULE', 'PURPLE MARTIN', 'PURPLE SWAMPHEN', 'QUETZAL',
+           'RAINBOW LORIKEET', 'RAZORBILL', 'RED FACED CORMORANT', 'RED FACED WARBLER', 'RED HEADED DUCK', 'RED HEADED WOODPECKER', 'RED HONEY CREEPER',
+           'RED THROATED BEE EATER', 'RED WINGED BLACKBIRD', 'RED WISKERED BULBUL', 'RING-NECKED PHEASANT', 'ROADRUNNER', 'ROBIN', 'ROCK DOVE', 'ROSY FACED LOVEBIRD',
+           'ROUGH LEG BUZZARD', 'RUBY THROATED HUMMINGBIRD', 'RUFOUS KINGFISHER', 'RUFUOS MOTMOT', 'SAND MARTIN', 'SCARLET IBIS', 'SCARLET MACAW', 'SHOEBILL',
+           'SMITHS LONGSPUR', 'SNOWY EGRET', 'SNOWY OWL', 'SORA', 'SPANGLED COTINGA', 'SPLENDID WREN', 'SPOON BILED SANDPIPER', 'SPOONBILL', 'STEAMER DUCK',
+           'STORK BILLED KINGFISHER', 'STRAWBERRY FINCH', 'STRIPPED SWALLOW', 'SUPERB STARLING', 'TAIWAN MAGPIE', 'TAKAHE', 'TASMANIAN HEN', 'TEAL DUCK',
+           'TIT MOUSE', 'TOUCHAN', 'TOWNSENDS WARBLER', 'TREE SWALLOW', 'TRUMPTER SWAN', 'TURKEY VULTURE', 'TURQUOISE MOTMOT', 'VARIED THRUSH',
+           'VENEZUELIAN TROUPIAL', 'VERMILION FLYCATHER', 'VIOLET GREEN SWALLOW', 'WATTLED CURASSOW', 'WHIMBREL', 'WHITE CHEEKED TURACO',
+           'WHITE NECKED RAVEN', 'WHITE TAILED TROPIC', 'WILD TURKEY', 'WILSONS BIRD OF PARADISE', 'WOOD DUCK', 'YELLOW CACIQUE', 'YELLOW HEADED BLACKBIRD']
+"""
 path = Path(__file__).parent
 
 app = Starlette()
 app.add_middleware(CORSMiddleware, allow_origins=['*'], allow_headers=['X-Requested-With', 'Content-Type'])
 app.mount('/static', StaticFiles(directory='app/static'))
 
-
+"""
 async def download_file(url, dest):
-    if dest.exists(): return
     async with aiohttp.ClientSession() as session:
         async with session.get(url) as response:
             data = await response.read()
@@ -30,9 +63,9 @@ async def download_file(url, dest):
 
 
 async def setup_learner():
-    await download_file(export_file_url, path / export_file_name)
+    await download_file(export_file_url, path / export_file_name_2)
     try:
-        learn = load_learner(path, export_file_name)
+        learn = pickle.load(open((path / 'models/model.sav'), 'rb'))
         return learn
     except RuntimeError as e:
         if len(e.args) > 0 and 'CPU-only machine' in e.args[0]:
@@ -47,23 +80,67 @@ async def setup_learner():
 tasks = [asyncio.ensure_future(setup_learner())]
 learn = loop.run_until_complete(asyncio.gather(*tasks))[0]
 loop.close()
-
-
+"""
+#hey
+#rool
 @app.route('/')
 async def homepage(request):
     html_file = path / 'view' / 'index.html'
     return HTMLResponse(html_file.open().read())
 
+@app.route('/aboutus')
+async def homepage(request):
+    html_file = path / 'view' / 'aboutus.html'
+    return HTMLResponse(html_file.open().read())
+
+@app.route('/aboutp')
+async def homepage(request):
+    html_file = path / 'view' / 'aboutp.html'
+    return HTMLResponse(html_file.open().read())
 
-@app.route('/analyze', methods=['POST'])
-async def analyze(request):
+@app.route('/classifier')
+async def homepage(request):
+    html_file = path / 'view' / 'classifier.html'
+    return HTMLResponse(html_file.open().read())
+
+@app.route('/analyze_cnn', methods=['POST'])
+async def analyze_cnn(request):
     img_data = await request.form()
     img_bytes = await (img_data['file'].read())
     img = open_image(BytesIO(img_bytes))
-    prediction = learn.predict(img)[0]
-    return JSONResponse({'result': str(prediction)})
+    learn2 = load_learner(path / 'models', export_file_name)
+    pred_class, pred_idx, outputs = learn2.predict(img) #[0]
+    prediction = learn2.predict(img)[0]
+    pred_probs = outputs/sum(outputs)
+    pred_probs = pred_probs.tolist()
+    predictions = []
+    for image_class, output, prob in zip(learn2.data.classes, outputs.tolist(), pred_probs):
+        output = round(output, 1)
+        prob = round(prob, 2)
+        predictions.append(
+            {"class": image_class.replace("_", " "), "output": output, "prob": prob}
+        )
+
+    predictions = sorted(predictions, key=lambda x: x["output"], reverse=True)
+    predictions = predictions[0:2]
+    #print({"class": str(pred_class), "predictions": predictions})
+    predictions = (predictions)[0]
+    return JSONResponse({'result': [str(predictions['class']), str(predictions['prob']*100)]})
+
+@app.route('/analyze_knn', methods=['POST'])
+async def analyze_knn(request):
+    img_data = await request.form()
+    img_bytes = await (img_data['file'].read())
+    img = IM.open(BytesIO(img_bytes))
+    img = img.resize((224, 224))
+    img = np.array(img)
+    img = img.reshape(1, 50176)
+    learn = pickle.load(open(path / 'models/model.sav', 'rb'))
+    output_class = learn.predict(img)[0]
+
+    return JSONResponse({'result': str(output_class)})
 
 
 if __name__ == '__main__':
     if 'serve' in sys.argv:
-        uvicorn.run(app=app, host='0.0.0.0', port=5000, log_level="info")
+        uvicorn.run(app=app, host='0.0.0.0', port=5000, log_level="info")

app/static/client.js

@@ -14,28 +14,85 @@ function showPicked(input) {
   reader.readAsDataURL(input.files[0]);
 }
 
-function analyze() {
+function analyze() { 
+  console.log("Hei Elise <3")
   var uploadFiles = el("file-input").files;
   if (uploadFiles.length !== 1) alert("Please select a file to analyze!");
 
   el("analyze-button").innerHTML = "Analyzing...";
+
+
   var xhr = new XMLHttpRequest();
   var loc = window.location;
-  xhr.open("POST", `${loc.protocol}//${loc.hostname}:${loc.port}/analyze`,
+  xhr.open("POST", `${loc.protocol}//${loc.hostname}:${loc.port}/analyze_cnn`,
     true);
   xhr.onerror = function() {
     alert(xhr.responseText);
   };
   xhr.onload = function(e) {
     if (this.readyState === 4) {
       var response = JSON.parse(e.target.responseText);
-      el("result-label").innerHTML = `Result = ${response["result"]}`;
+      // var response = {"result":[{'class': 'PNEUMONIA', 'output': 0.9, 'prob': 0.93}, {'class': 'NORMAL', 'output': 0.1, 'prob': 0.07}]}
+      var result = response["result"]
+      el("result-label").innerHTML = `Result = ${result}`;
+      var modal = document.getElementById("myModal");
+      modal.style.display = "block";
+      el("state").innerHTML = `${result[0]}`;
+      // state.innerHTML = response["result"][0].class
+      el("percent").innerHTML = result[1];
     }
     el("analyze-button").innerHTML = "Analyze";
   };
 
+
   var fileData = new FormData();
   fileData.append("file", uploadFiles[0]);
   xhr.send(fileData);
 }
+// Top navigation bar
+$(function(){
+  var str = '#len'; //increment by 1 up to 1-nelemnts
+  $(document).ready(function(){
+    var i, stop;
+    i = 1;
+    stop = 4; //num elements
+    setInterval(function(){
+      if (i > stop){
+        return;
+      }
+      $('#len'+(i++)).toggleClass('bounce');
+    }, 500)
+  });
+});
+
+// Modal for results
+
+// When the user clicks the button, open the modal 
+// External modal-button for testing locally
+// function openModal(){
+  // var response = JSON.parse(e.target.responseText);
+  // var response = {"result":[{'class': 'PNEUMONIA', 'output': 0.9, 'prob': 0.93}, {'class': 'NORMAL', 'output': 0.1, 'prob': 0.07}]}
+  // var response = {"result":[{'class': 'NORMAL', 'output': 1.0, 'prob': 1.0}, {'class': 'PNEUMONIA', 'output': 0.0, 'prob': 0.0}]}
+  // el("result-label").innerHTML = `Result = ${response["result"]}`;
+  // var modal = document.getElementById("myModal");
+  // modal.style.display = "block";
+  // el("state").innerHTML = response["result"][0].class
+  // state.innerHTML = response["result"][0].class
+  // el("percent").innerHTML = response["result"][0].prob * 100
+// }
+
+
+// When the user clicks on <span> (x), close the modal
+function closeModal(){
+  var modal = document.getElementById("myModal");
+  modal.style.display = "none";
+}
+
+// When the user clicks anywhere outside of the modal, close it
 
+window.onclick = function(event) {
+  var modal = document.getElementById("myModal");
+  if (event.target === modal) {
+    modal.style.display = "none";
+  }
+}