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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
import gradio as gr
# --- Define the MLP_one CNN architecture ---
class MLP_one(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# --- Load trained model weights ---
model = MLP_one()
model.load_state_dict(torch.load("model.pth", map_location="cpu"))
model.eval()
# --- CIFAR-10 class names ---
classes = [
"airplane", "automobile", "bird", "cat", "deer",
"dog", "frog", "horse", "ship", "truck"
]
# --- Transform pipeline ---
transform = transforms.Compose([
transforms.Resize((32, 32)), # resize any image to 32x32
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# --- Prediction function ---
def predict(image):
image = image.convert("RGB")
x = transform(image).unsqueeze(0) # (1, 3, 32, 32)
with torch.no_grad():
outputs = model(x) # tensor shape [1, 10]
probs = torch.nn.functional.softmax(outputs, dim=1) # apply softmax
probs = probs[0].cpu().numpy() # convert to numpy for Gradio
return {classes[i]: float(probs[i]) for i in range(10)}
# --- Gradio Interface ---
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Upload any image"),
outputs=gr.Label(num_top_classes=3),
title="CIFAR-10 Image Classifier (MLP_one)",
description=(
"Upload any image (JPG, PNG, etc.) and this model will resize it to 32×32 "
"and predict the closest CIFAR-10 class."
)
)
demo.launch()
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