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3c3c15a d2d676d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | import io
import json
import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
# ─── App setup ──────────────────────────────────────────────────────────────
app = FastAPI(title="ISL Recognition API", version="1.0.0")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Lock this to your Vercel URL in production
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# ─── Model loader ────────────────────────────────────────────────────────────
def build_model(arch: str, num_classes: int) -> nn.Module:
arch = arch.lower()
if arch == "resnet18":
model = models.resnet18(weights=None)
model.fc = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(model.fc.in_features, num_classes)
)
elif arch == "mobilenet_v2":
model = models.mobilenet_v2(weights=None)
model.classifier[1] = nn.Linear(model.classifier[1].in_features, num_classes)
elif arch == "efficientnet_b0":
model = models.efficientnet_b0(weights=None)
model.classifier[1] = nn.Linear(model.classifier[1].in_features, num_classes)
elif arch == "vgg16":
model = models.vgg16(weights=None)
model.classifier[6] = nn.Linear(model.classifier[6].in_features, num_classes)
elif arch in ("cnn", "cnn_dropout"):
# Simple custom CNN
class _CNN(nn.Module):
def __init__(self, n, dropout=False):
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 32, 3, padding=1), nn.BatchNorm2d(32), nn.ReLU(True), nn.MaxPool2d(2),
nn.Conv2d(32, 64, 3, padding=1), nn.BatchNorm2d(64), nn.ReLU(True), nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3, padding=1), nn.BatchNorm2d(128), nn.ReLU(True), nn.MaxPool2d(2),
nn.Conv2d(128, 256, 3, padding=1), nn.BatchNorm2d(256), nn.ReLU(True), nn.MaxPool2d(2),
)
layers = [nn.AdaptiveAvgPool2d((1, 1)), nn.Flatten()]
if dropout:
layers.append(nn.Dropout(0.5))
layers.append(nn.Linear(256, n))
self.classifier = nn.Sequential(*layers)
def forward(self, x):
return self.classifier(self.features(x))
model = _CNN(num_classes, dropout=(arch == "cnn_dropout"))
else:
raise ValueError(f"Unknown architecture: {arch}")
return model
# ─── Load checkpoint on startup ──────────────────────────────────────────────
MODEL_PATH = "isl_best_model.pth"
device = torch.device("cpu")
checkpoint = torch.load(MODEL_PATH, map_location=device)
ARCH = checkpoint["arch"]
NUM_CLASSES = checkpoint["num_classes"]
CLASS_NAMES = checkpoint["class_names"]
model = build_model(ARCH, NUM_CLASSES)
model.load_state_dict(checkpoint["state_dict"])
model.eval()
model.to(device)
print(f"✅ Loaded model: {ARCH} | Classes: {NUM_CLASSES} | Val Acc: {checkpoint.get('val_acc', 'N/A')}")
# ─── Inference transform (matches val_transform in notebook) ─────────────────
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
# ─── Routes ──────────────────────────────────────────────────────────────────
@app.get("/")
def root():
return {
"message": "ISL Recognition API is running 🤟",
"model": ARCH,
"num_classes": NUM_CLASSES,
"val_acc": checkpoint.get("val_acc"),
}
@app.get("/health")
def health():
return {"status": "ok"}
@app.get("/classes")
def get_classes():
return {"classes": CLASS_NAMES}
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
# Validate file type
if file.content_type not in ("image/jpeg", "image/png", "image/jpg", "image/webp"):
raise HTTPException(status_code=400, detail="Only JPEG/PNG images are accepted.")
try:
contents = await file.read()
image = Image.open(io.BytesIO(contents)).convert("RGB")
except Exception:
raise HTTPException(status_code=400, detail="Could not read image file.")
tensor = transform(image).unsqueeze(0).to(device) # [1, 3, 224, 224]
with torch.no_grad():
logits = model(tensor)
probs = torch.softmax(logits, dim=1)[0]
top5_probs, top5_idx = torch.topk(probs, k=min(5, NUM_CLASSES))
return JSONResponse({
"prediction": CLASS_NAMES[top5_idx[0].item()],
"confidence": round(top5_probs[0].item() * 100, 2),
"top5": [
{
"label": CLASS_NAMES[idx.item()],
"confidence": round(prob.item() * 100, 2)
}
for prob, idx in zip(top5_probs, top5_idx)
],
"model_used": ARCH,
})
@app.post("/live")
async def live_predict(file: UploadFile = File(...)):
# Validate file type
if file.content_type not in ("image/jpeg", "image/png", "image/jpg", "image/webp"):
raise HTTPException(status_code=400, detail="Only JPEG/PNG images are accepted.")
try:
contents = await file.read()
image = Image.open(io.BytesIO(contents)).convert("RGB")
except Exception:
raise HTTPException(status_code=400, detail="Could not read image file.")
tensor = transform(image).unsqueeze(0).to(device) # [1, 3, 224, 224]
with torch.no_grad():
logits = model(tensor)
probs = torch.softmax(logits, dim=1)[0]
top5_probs, top5_idx = torch.topk(probs, k=min(5, NUM_CLASSES))
return JSONResponse({
"prediction": CLASS_NAMES[top5_idx[0].item()],
"confidence": round(top5_probs[0].item() * 100, 2),
"top5": [
{
"label": CLASS_NAMES[idx.item()],
"confidence": round(prob.item() * 100, 2)
}
for prob, idx in zip(top5_probs, top5_idx)
],
"model_used": ARCH,
}) |