Merge branch 'main' of https://huggingface.co/spaces/reidddd/carDamageDetection

.gitignore

@@ -1,3 +1,7 @@
 **/model_final.pth
 **/__pycache__
-**/model_final.ptht7kgwblo.part
+**/*.png
+**/*.jpeg
+**/*.jpg
+**/model_final.ptht7kgwblo.part
+**/model_final.ptht7kgwblo

app.py

@@ -7,16 +7,72 @@ from roboflow import Roboflow
 import supervision as sv
 import cv2
 import tempfile
+import gdown
 import os
 import requests
 import requests
 import cloudinary
+import model
 import cloudinary.uploader
 from a import main
-
+import numpy as np
+import torchvision.transforms as transforms
+import pandas as pd
+import nibabel as nib
+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+import cv2
+from PIL import Image
+from sklearn.model_selection import train_test_split
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.transforms as transforms
+import torchvision.models as models
+from torch.utils.data import Dataset, DataLoader
+import pandas as pd
+from PIL import Image
+import os
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
 # Initialize Flask app
 app = Flask(__name__)
 
+GDRIVE_MODEL_URL = "https://drive.google.com/uc?id=1fzKneepaRt_--dzamTcDBM-9d3_dLX7z"
+LOCAL_MODEL_PATH = "checkpoint32.pth"
+d = "https://drive.google.com/uc?id=1GfrlFNoa7E4liMHyMuF73nA21yT9SNSb"
+
+
+def download_file_from_google_drive():
+    gdown.download(GDRIVE_MODEL_URL, LOCAL_MODEL_PATH, quiet=False)
+
+
+da = "a.pth"
+
+
+def download_file_from_google_drived():
+    gdown.download(d, da, quiet=False)
+
+
+def download_model():
+    if not os.path.exists(LOCAL_MODEL_PATH):
+        response = requests.get(GDRIVE_MODEL_URL, stream=True)
+        if response.status_code == 200:
+            with open(LOCAL_MODEL_PATH, "wb") as f:
+                f.write(response.content)
+        else:
+            raise Exception(
+                f"Failed to download model from Google Drive: {response.status_code}"
+            )
+
+
+download_file_from_google_drive()
+download_file_from_google_drived()
+
 
 @app.route("/")
 def home():
@@ -26,6 +82,7 @@ def home():
 @app.route("/fetch-image", methods=["POST"])
 def fetchImage():
     file = None
+    url = ""
     if "url" in request.form:
         url = request.form["url"]
         response = requests.get(url)
@@ -143,7 +200,7 @@ def fetchImage():
                 part_name, 0
             )  # Default to 0 if part not in dict
             repair_cost = (damage_percentage / 100) * 10 * base_cost
-            total_cost += repair_cost
+            total_cost += round(repair_cost, ndigits=1)
 
             print(
                 f"Damage {i + 1} - {part_name}: {damage_percentage:.2f}% damaged, Cost: ${repair_cost:.2f}"
@@ -186,5 +243,84 @@ def generate_report():
     return jsonify({"message": "Something happened!."}), 404
 
 
+@app.route("/ms-detection", methods=["POST"])
+def predict():
+    file = request.files["file"]
+
+    if not file:
+        return jsonify({"error": "file not uploaded"}), 400
+
+    # Save file temporarily
+    temp_path = os.path.join(tempfile.gettempdir(), file.filename)
+    file.save(temp_path)
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+    ])
+    if file.filename.lower().endswith((".png", ".jpg", ".jpeg")):
+        image = Image.open(temp_path)
+        image_save_path = os.path.join(
+            tempfile.gettempdir(), file.filename.lower())
+        image.save(image_save_path)
+
+    def is_mri_image(image_path):
+        img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+
+        if img is None:
+            return False  # Invalid image
+
+        # Apply Canny edge detection
+        edges = cv2.Canny(img, 50, 150)
+
+        # Calculate edge density (MRI images have high edge presence)
+        edge_density = np.sum(edges > 0) / edges.size
+        print(edge_density)
+        return edge_density > 0.05
+
+    if (is_mri_image(temp_path)):
+        return jsonify({"message": "Not an mri image", "confidence": 0.95, "saved_path": image_save_path})
+    a, b = model.check_file(temp_path)
+
+    class ResNetRegression(nn.Module):
+        def __init__(self):
+            super(ResNetRegression, self).__init__()
+            self.model = models.resnet34(pretrained=True)
+            in_features = self.model.fc.in_features
+            # Change output layer for regression
+            self.model.fc = nn.Linear(in_features, 1)
+
+        def forward(self, x):
+            return self.model(x)
+
+    # Initialize Model, Loss, and Optimizer
+    model_new = ResNetRegression()
+    checkpoint = torch.load(
+        "/home/user/app/a.pth", weights_only=False, map_location=torch.device('cpu'))
+
+    def remove_module_from_checkpoint(checkpoint):
+        new_state_dict = {}
+        print(checkpoint.keys())
+        for key, value in checkpoint.items():
+            new_key = key.replace("module.", "")
+            new_state_dict[new_key] = value
+        checkpoint = new_state_dict
+        return checkpoint
+    checkpoint = remove_module_from_checkpoint(checkpoint)
+    model_new.load_state_dict(checkpoint)
+    image = Image.open(temp_path).convert("RGB")
+    output = model_new(transform(image).unsqueeze(0))
+    stage = output.item()
+    if not a == "No ms detected":
+        if stage <= 2.0:
+            stage = "Mild"
+        elif stage >= 2.0 and stage <= 3.2:
+            stage = "Moderate"
+        else:
+            stage = "Severe"
+    else:
+        stage = "No ms detected"
+    return jsonify({"message": a, "confidence": b, "stage": stage, "saved_path": image_save_path})
+
+
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860)

imagenet_classes.txt

@@ -0,0 +1,1000 @@
+tench
+goldfish
+great white shark
+tiger shark
+hammerhead
+electric ray
+stingray
+cock
+hen
+ostrich
+brambling
+goldfinch
+house finch
+junco
+indigo bunting
+robin
+bulbul
+jay
+magpie
+chickadee
+water ouzel
+kite
+bald eagle
+vulture
+great grey owl
+European fire salamander
+common newt
+eft
+spotted salamander
+axolotl
+bullfrog
+tree frog
+tailed frog
+loggerhead
+leatherback turtle
+mud turtle
+terrapin
+box turtle
+banded gecko
+common iguana
+American chameleon
+whiptail
+agama
+frilled lizard
+alligator lizard
+Gila monster
+green lizard
+African chameleon
+Komodo dragon
+African crocodile
+American alligator
+triceratops
+thunder snake
+ringneck snake
+hognose snake
+green snake
+king snake
+garter snake
+water snake
+vine snake
+night snake
+boa constrictor
+rock python
+Indian cobra
+green mamba
+sea snake
+horned viper
+diamondback
+sidewinder
+trilobite
+harvestman
+scorpion
+black and gold garden spider
+barn spider
+garden spider
+black widow
+tarantula
+wolf spider
+tick
+centipede
+black grouse
+ptarmigan
+ruffed grouse
+prairie chicken
+peacock
+quail
+partridge
+African grey
+macaw
+sulphur-crested cockatoo
+lorikeet
+coucal
+bee eater
+hornbill
+hummingbird
+jacamar
+toucan
+drake
+red-breasted merganser
+goose
+black swan
+tusker
+echidna
+platypus
+wallaby
+koala
+wombat
+jellyfish
+sea anemone
+brain coral
+flatworm
+nematode
+conch
+snail
+slug
+sea slug
+chiton
+chambered nautilus
+Dungeness crab
+rock crab
+fiddler crab
+king crab
+American lobster
+spiny lobster
+crayfish
+hermit crab
+isopod
+white stork
+black stork
+spoonbill
+flamingo
+little blue heron
+American egret
+bittern
+crane
+limpkin
+European gallinule
+American coot
+bustard
+ruddy turnstone
+red-backed sandpiper
+redshank
+dowitcher
+oystercatcher
+pelican
+king penguin
+albatross
+grey whale
+killer whale
+dugong
+sea lion
+Chihuahua
+Japanese spaniel
+Maltese dog
+Pekinese
+Shih-Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound
+basset
+beagle
+bloodhound
+bluetick
+black-and-tan coonhound
+Walker hound
+English foxhound
+redbone
+borzoi
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound
+Norwegian elkhound
+otterhound
+Saluki
+Scottish deerhound
+Weimaraner
+Staffordshire bullterrier
+American Staffordshire terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire-haired fox terrier
+Lakeland terrier
+Sealyham terrier
+Airedale
+cairn
+Australian terrier
+Dandie Dinmont
+Boston bull
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier
+Tibetan terrier
+silky terrier
+soft-coated wheaten terrier
+West Highland white terrier
+Lhasa
+flat-coated retriever
+curly-coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short-haired pointer
+vizsla
+English setter
+Irish setter
+Gordon setter
+Brittany spaniel
+clumber
+English springer
+Welsh springer spaniel
+cocker spaniel
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog
+Shetland sheepdog
+collie
+Border collie
+Bouvier des Flandres
+Rottweiler
+German shepherd
+Doberman
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard
+Eskimo dog
+malamute
+Siberian husky
+dalmatian
+affenpinscher
+basenji
+pug
+Leonberg
+Newfoundland
+Great Pyrenees
+Samoyed
+Pomeranian
+chow
+keeshond
+Brabancon griffon
+Pembroke
+Cardigan
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+timber wolf
+white wolf
+red wolf
+coyote
+dingo
+dhole
+African hunting dog
+hyena
+red fox
+kit fox
+Arctic fox
+grey fox
+tabby
+tiger cat
+Persian cat
+Siamese cat
+Egyptian cat
+cougar
+lynx
+leopard
+snow leopard
+jaguar
+lion
+tiger
+cheetah
+brown bear
+American black bear
+ice bear
+sloth bear
+mongoose
+meerkat
+tiger beetle
+ladybug
+ground beetle
+long-horned beetle
+leaf beetle
+dung beetle
+rhinoceros beetle
+weevil
+fly
+bee
+ant
+grasshopper
+cricket
+walking stick
+cockroach
+mantis
+cicada
+leafhopper
+lacewing
+dragonfly
+damselfly
+admiral
+ringlet
+monarch
+cabbage butterfly
+sulphur butterfly
+lycaenid
+starfish
+sea urchin
+sea cucumber
+wood rabbit
+hare
+Angora
+hamster
+porcupine
+fox squirrel
+marmot
+beaver
+guinea pig
+sorrel
+zebra
+hog
+wild boar
+warthog
+hippopotamus
+ox
+water buffalo
+bison
+ram
+bighorn
+ibex
+hartebeest
+impala
+gazelle
+Arabian camel
+llama
+weasel
+mink
+polecat
+black-footed ferret
+otter
+skunk
+badger
+armadillo
+three-toed sloth
+orangutan
+gorilla
+chimpanzee
+gibbon
+siamang
+guenon
+patas
+baboon
+macaque
+langur
+colobus
+proboscis monkey
+marmoset
+capuchin
+howler monkey
+titi
+spider monkey
+squirrel monkey
+Madagascar cat
+indri
+Indian elephant
+African elephant
+lesser panda
+giant panda
+barracouta
+eel
+coho
+rock beauty
+anemone fish
+sturgeon
+gar
+lionfish
+puffer
+abacus
+abaya
+academic gown
+accordion
+acoustic guitar
+aircraft carrier
+airliner
+airship
+altar
+ambulance
+amphibian
+analog clock
+apiary
+apron
+ashcan
+assault rifle
+backpack
+bakery
+balance beam
+balloon
+ballpoint
+Band Aid
+banjo
+bannister
+barbell
+barber chair
+barbershop
+barn
+barometer
+barrel
+barrow
+baseball
+basketball
+bassinet
+bassoon
+bathing cap
+bath towel
+bathtub
+beach wagon
+beacon
+beaker
+bearskin
+beer bottle
+beer glass
+bell cote
+bib
+bicycle-built-for-two
+bikini
+binder
+binoculars
+birdhouse
+boathouse
+bobsled
+bolo tie
+bonnet
+bookcase
+bookshop
+bottlecap
+bow
+bow tie
+brass
+brassiere
+breakwater
+breastplate
+broom
+bucket
+buckle
+bulletproof vest
+bullet train
+butcher shop
+cab
+caldron
+candle
+cannon
+canoe
+can opener
+cardigan
+car mirror
+carousel
+carpenter's kit
+carton
+car wheel
+cash machine
+cassette
+cassette player
+castle
+catamaran
+CD player
+cello
+cellular telephone
+chain
+chainlink fence
+chain mail
+chain saw
+chest
+chiffonier
+chime
+china cabinet
+Christmas stocking
+church
+cinema
+cleaver
+cliff dwelling
+cloak
+clog
+cocktail shaker
+coffee mug
+coffeepot
+coil
+combination lock
+computer keyboard
+confectionery
+container ship
+convertible
+corkscrew
+cornet
+cowboy boot
+cowboy hat
+cradle
+crane
+crash helmet
+crate
+crib
+Crock Pot
+croquet ball
+crutch
+cuirass
+dam
+desk
+desktop computer
+dial telephone
+diaper
+digital clock
+digital watch
+dining table
+dishrag
+dishwasher
+disk brake
+dock
+dogsled
+dome
+doormat
+drilling platform
+drum
+drumstick
+dumbbell
+Dutch oven
+electric fan
+electric guitar
+electric locomotive
+entertainment center
+envelope
+espresso maker
+face powder
+feather boa
+file
+fireboat
+fire engine
+fire screen
+flagpole
+flute
+folding chair
+football helmet
+forklift
+fountain
+fountain pen
+four-poster
+freight car
+French horn
+frying pan
+fur coat
+garbage truck
+gasmask
+gas pump
+goblet
+go-kart
+golf ball
+golfcart
+gondola
+gong
+gown
+grand piano
+greenhouse
+grille
+grocery store
+guillotine
+hair slide
+hair spray
+half track
+hammer
+hamper
+hand blower
+hand-held computer
+handkerchief
+hard disc
+harmonica
+harp
+harvester
+hatchet
+holster
+home theater
+honeycomb
+hook
+hoopskirt
+horizontal bar
+horse cart
+hourglass
+iPod
+iron
+jack-o'-lantern
+jean
+jeep
+jersey
+jigsaw puzzle
+jinrikisha
+joystick
+kimono
+knee pad
+knot
+lab coat
+ladle
+lampshade
+laptop
+lawn mower
+lens cap
+letter opener
+library
+lifeboat
+lighter
+limousine
+liner
+lipstick
+Loafer
+lotion
+loudspeaker
+loupe
+lumbermill
+magnetic compass
+mailbag
+mailbox
+maillot
+maillot
+manhole cover
+maraca
+marimba
+mask
+matchstick
+maypole
+maze
+measuring cup
+medicine chest
+megalith
+microphone
+microwave
+military uniform
+milk can
+minibus
+miniskirt
+minivan
+missile
+mitten
+mixing bowl
+mobile home
+Model T
+modem
+monastery
+monitor
+moped
+mortar
+mortarboard
+mosque
+mosquito net
+motor scooter
+mountain bike
+mountain tent
+mouse
+mousetrap
+moving van
+muzzle
+nail
+neck brace
+necklace
+nipple
+notebook
+obelisk
+oboe
+ocarina
+odometer
+oil filter
+organ
+oscilloscope
+overskirt
+oxcart
+oxygen mask
+packet
+paddle
+paddlewheel
+padlock
+paintbrush
+pajama
+palace
+panpipe
+paper towel
+parachute
+parallel bars
+park bench
+parking meter
+passenger car
+patio
+pay-phone
+pedestal
+pencil box
+pencil sharpener
+perfume
+Petri dish
+photocopier
+pick
+pickelhaube
+picket fence
+pickup
+pier
+piggy bank
+pill bottle
+pillow
+ping-pong ball
+pinwheel
+pirate
+pitcher
+plane
+planetarium
+plastic bag
+plate rack
+plow
+plunger
+Polaroid camera
+pole
+police van
+poncho
+pool table
+pop bottle
+pot
+potter's wheel
+power drill
+prayer rug
+printer
+prison
+projectile
+projector
+puck
+punching bag
+purse
+quill
+quilt
+racer
+racket
+radiator
+radio
+radio telescope
+rain barrel
+recreational vehicle
+reel
+reflex camera
+refrigerator
+remote control
+restaurant
+revolver
+rifle
+rocking chair
+rotisserie
+rubber eraser
+rugby ball
+rule
+running shoe
+safe
+safety pin
+saltshaker
+sandal
+sarong
+sax
+scabbard
+scale
+school bus
+schooner
+scoreboard
+screen
+screw
+screwdriver
+seat belt
+sewing machine
+shield
+shoe shop
+shoji
+shopping basket
+shopping cart
+shovel
+shower cap
+shower curtain
+ski
+ski mask
+sleeping bag
+slide rule
+sliding door
+slot
+snorkel
+snowmobile
+snowplow
+soap dispenser
+soccer ball
+sock
+solar dish
+sombrero
+soup bowl
+space bar
+space heater
+space shuttle
+spatula
+speedboat
+spider web
+spindle
+sports car
+spotlight
+stage
+steam locomotive
+steel arch bridge
+steel drum
+stethoscope
+stole
+stone wall
+stopwatch
+stove
+strainer
+streetcar
+stretcher
+studio couch
+stupa
+submarine
+suit
+sundial
+sunglass
+sunglasses
+sunscreen
+suspension bridge
+swab
+sweatshirt
+swimming trunks
+swing
+switch
+syringe
+table lamp
+tank
+tape player
+teapot
+teddy
+television
+tennis ball
+thatch
+theater curtain
+thimble
+thresher
+throne
+tile roof
+toaster
+tobacco shop
+toilet seat
+torch
+totem pole
+tow truck
+toyshop
+tractor
+trailer truck
+tray
+trench coat
+tricycle
+trimaran
+tripod
+triumphal arch
+trolleybus
+trombone
+tub
+turnstile
+typewriter keyboard
+umbrella
+unicycle
+upright
+vacuum
+vase
+vault
+velvet
+vending machine
+vestment
+viaduct
+violin
+volleyball
+waffle iron
+wall clock
+wallet
+wardrobe
+warplane
+washbasin
+washer
+water bottle
+water jug
+water tower
+whiskey jug
+whistle
+wig
+window screen
+window shade
+Windsor tie
+wine bottle
+wing
+wok
+wooden spoon
+wool
+worm fence
+wreck
+yawl
+yurt
+web site
+comic book
+crossword puzzle
+street sign
+traffic light
+book jacket
+menu
+plate
+guacamole
+consomme
+hot pot
+trifle
+ice cream
+ice lolly
+French loaf
+bagel
+pretzel
+cheeseburger
+hotdog
+mashed potato
+head cabbage
+broccoli
+cauliflower
+zucchini
+spaghetti squash
+acorn squash
+butternut squash
+cucumber
+artichoke
+bell pepper
+cardoon
+mushroom
+Granny Smith
+strawberry
+orange
+lemon
+fig
+pineapple
+banana
+jackfruit
+custard apple
+pomegranate
+hay
+carbonara
+chocolate sauce
+dough
+meat loaf
+pizza
+potpie
+burrito
+red wine
+espresso
+cup
+eggnog
+alp
+bubble
+cliff
+coral reef
+geyser
+lakeside
+promontory
+sandbar
+seashore
+valley
+volcano
+ballplayer
+groom
+scuba diver
+rapeseed
+daisy
+yellow lady's slipper
+corn
+acorn
+hip
+buckeye
+coral fungus
+agaric
+gyromitra
+stinkhorn
+earthstar
+hen-of-the-woods
+bolete
+ear
+toilet tissue

model.py

@@ -0,0 +1,164 @@
+
+from torch.utils.data import DataLoader
+from torchvision import transforms
+import numpy as np
+import pandas as pd
+import os
+import cv2
+from sklearn.utils import shuffle
+from sklearn.model_selection import train_test_split
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+import torch
+import torch.nn as nn
+
+
+class HybridCNNViT(nn.Module):
+    def __init__(self, in_channels: int, num_classes: int):
+        super(HybridCNNViT, self).__init__()
+
+        self.conv1 = nn.Conv2d(
+            in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+
+        self.conv2 = nn.Conv2d(64, 128, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(128)
+
+        self.conv3 = nn.Conv2d(128, 128, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(128)
+
+        self.conv4 = nn.Conv2d(128, 256, kernel_size=3,
+                               stride=2, padding=1, bias=False)
+        self.bn4 = nn.BatchNorm2d(256)
+
+        self.conv5 = nn.Conv2d(256, 256, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+        self.bn5 = nn.BatchNorm2d(256)
+
+        self.conv6 = nn.Conv2d(256, 512, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+        self.bn6 = nn.BatchNorm2d(512)
+
+        self.conv7 = nn.Conv2d(512, 512, kernel_size=3,
+                               stride=2, padding=1, bias=False)
+        self.bn7 = nn.BatchNorm2d(512)
+
+        # Optional MaxPooling (can be removed if strictly no max pooling)
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.classifier_conv = nn.Conv2d(
+            512, num_classes, kernel_size=1, stride=1, padding=0, bias=False)
+
+        self.classifier = nn.Sequential(
+            nn.AdaptiveAvgPool2d((1, 1)),
+            nn.Flatten(),
+            nn.Dropout(0.5)
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.relu(self.bn1(self.conv1(x)))
+        x = self.relu(self.bn2(self.conv2(x)))
+        x = self.relu(self.bn3(self.conv3(x)))
+        x = self.relu(self.bn4(self.conv4(x)))
+        x = self.relu(self.bn5(self.conv5(x)))
+        x = self.relu(self.bn6(self.conv6(x)))
+        x = self.relu(self.bn7(self.conv7(x)))
+
+        x = self.maxpool(x)  # Comment this line if no max pooling is needed
+
+        x = self.classifier_conv(x)
+        x = self.classifier(x)
+
+        return x
+
+
+def load_and_pad_single_image(image_path, img_size=(224, 224)):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    img = cv2.imread(image_path)
+    if img is None:
+        raise ValueError(f"Could not read image: {image_path}")
+    img = cv2.resize(img, img_size)
+    return np.array(img)
+
+
+def check_file(image_path):
+    # image_path = "d/Control-Axial/C-A (2).png"
+
+    # Load and preprocess the single image
+    image = load_and_pad_single_image(image_path)
+    image = np.expand_dims(image, axis=0)  # Convert to batch format
+
+    # Duplicate the image 10 times
+    data = np.repeat(image, 10, axis=0)
+
+    # Normalize and transform the image
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[
+                             0.229, 0.224, 0.225])
+    ])
+
+    data = torch.tensor(data, dtype=torch.float32).permute(
+        0, 3, 1, 2).to(device)
+    # Placeholder labels for 10 images
+    labels = torch.tensor([0] * 10, dtype=torch.long).to(device)
+
+    data, labels = shuffle(data, labels, random_state=42)
+
+    train_data, test_data, train_labels, test_labels = train_test_split(
+        data, labels, test_size=0.2, random_state=42
+    )
+
+    train_labels = torch.tensor(train_labels, dtype=torch.long)
+    test_labels = torch.tensor(test_labels, dtype=torch.long)
+
+    batch_size = 1  # Since we are working with a single image
+    train_dataset = list(zip(train_data, train_labels))
+    test_dataset = list(zip(test_data, test_labels))
+    test_loader = DataLoader(
+        test_dataset, batch_size=batch_size, shuffle=False)
+
+    # Simple test with a model
+    output = ""
+
+    def test_model(model, test_loader, device):
+        global output
+        model.to(device)
+        model.eval()
+        with torch.no_grad():
+            for images, labels in test_loader:
+                images, labels = images.to(device), labels.to(device)
+                outputs = model(images)
+                _, predicted = torch.max(outputs.data, 1)
+                output = predicted
+                # Convert logits to probabilities
+                probabilities = F.softmax(outputs, dim=1)
+                # Get confidence score and prediction
+                confidence, d = torch.max(probabilities, 1)
+                print(confidence)
+                return predicted, confidence
+
+    def remove_module_from_checkpoint(checkpoint):
+        new_state_dict = {}
+        for key, value in checkpoint["model_state_dict"].items():
+            new_key = key.replace("module.", "")
+            new_state_dict[new_key] = value
+        checkpoint["model_state_dict"] = new_state_dict
+        return checkpoint
+
+    model = HybridCNNViT(3, 2)
+    checkpoint = torch.load(
+        "/home/user/app/checkpoint32.pth", weights_only=False, map_location=torch.device('cpu'))
+    checkpoint = remove_module_from_checkpoint(checkpoint)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.eval()
+    model.to(device)
+    model = nn.DataParallel(model)
+    output, confidence = test_model(model, test_loader, device)
+    return "No ms detected" if output.item() == 0 else "MS Detected", confidence.item()

requirements.txt

@@ -22,3 +22,7 @@ openai
 fpdf
 cloudinary
 PyPDF2
+scikit-learn
+pandas
+gdown
+nibabel

temp.py

@@ -17,7 +17,8 @@ import torch
 app = Flask(__name__)
 cfg = None
 # Google Drive file URL
-GDRIVE_MODEL_URL = "https://drive.google.com/uc?id=18aEDo-kWOBhg8mAhnbpFkuM6bmmrBH4E"  # Replace 'your-file-id' with the actual file ID from Google Drive
+# Replace 'your-file-id' with the actual file ID from Google Drive
+GDRIVE_MODEL_URL = "https://drive.google.com/uc?id=1fzKneepaRt_--dzamTcDBM-9d3_dLX7z"
 LOCAL_MODEL_PATH = "model_final.pth"
 
 
@@ -25,8 +26,8 @@ def download_file_from_google_drive(id, destination):
     gdown.download(GDRIVE_MODEL_URL, LOCAL_MODEL_PATH, quiet=False)
 
 
-file_id = "18aEDo-kWOBhg8mAhnbpFkuM6bmmrBH4E"
-destination = "model_final.pth"
+file_id = "1fzKneepaRt_--dzamTcDBM-9d3_dLX7z"
+destination = "checkpoint32.pth"
 download_file_from_google_drive(file_id, destination)
 
 
@@ -109,7 +110,8 @@ def upload():
             if j in merged_boxes:
                 continue
             iou = box_iou(
-                torch.tensor(boxes[i]).unsqueeze(0), torch.tensor(boxes[j]).unsqueeze(0)
+                torch.tensor(boxes[i]).unsqueeze(
+                    0), torch.tensor(boxes[j]).unsqueeze(0)
             ).item()
             if iou > iou_threshold:
                 merged_boxes.add(j)
@@ -150,8 +152,6 @@ def fetchImage():
     image = skio.imread(file)
     image_np = image
 
-    
-
     return jsonify(response)
 
 

test.py

@@ -0,0 +1,21 @@
+import cv2
+import numpy as np
+
+
+def is_mri_image(image_path):
+    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+
+    if img is None:
+        return False  # Invalid image
+
+    # Apply Canny edge detection
+    edges = cv2.Canny(img, 50, 150)
+
+    # Calculate edge density (MRI images have high edge presence)
+    edge_density = np.sum(edges > 0) / edges.size
+    print(edge_density)
+    return edge_density > 0.05  # Threshold from MRI dataset
+
+
+# Test on an MRI or non-MRI image
+print(is_mri_image("a.png"))