Spaces:
Runtime error
Runtime error
File size: 2,358 Bytes
ffa1bd9 df91a4c ffa1bd9 | 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 | from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
import torch
import torch.nn as nn
import numpy as np
class CNN(nn.Module):
def __init__(self, num_classes):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 7 * 7, 128)
self.fc2 = nn.Linear(128, num_classes)
self.dropout = nn.Dropout(0.3)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 64 * 7 * 7)
x = torch.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
model = CNN(10+26+26)
model.load_state_dict(torch.load("model_weights.pth", map_location="cpu"))
model.eval()
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Or specify your frontend URL
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/")
async def home():
return{"message": "api working"}
@app.post("/predict")
async def predict(request: Request):
data = await request.json()
pixels = data.get("input_image")
if pixels is None: return {"error_0": "Expected pixels but did not recieve data"}
if len(pixels) != 784: return {"error_1": "Exoected 784 values for 28x28 image"}
x = torch.tensor(pixels, dtype=torch.float32).view(1, 28, 28)
x = (x - 0.5) / 0.5
x = torch.flip(x.transpose(1, 2), dims=[2])
x = x.unsqueeze(0)
with torch.no_grad():
outputs = model(x)
probs = torch.softmax(outputs, dim=1)
pred_idx = int(torch.argmax(probs, dim=1).item())
confidence = float(probs[0, pred_idx].item())
emnist_labels = [
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z',
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'
]
return {
"prediction": emnist_labels[pred_idx],
"confidence": confidence
} |