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import gradio as gr
import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
import os
# 3. Define the model used for training
class VeggieNet(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.net = nn.Sequential(
nn.Flatten(),
nn.Linear(3 * 128 * 128, 512),
nn.BatchNorm1d(512),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(512, 256),
nn.BatchNorm1d(256),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(256, 128),
nn.BatchNorm1d(128),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(128, num_classes)
)
def forward(self, x):
return self.net(x)
# Manually loading the class names to match the dataset
class_names = ['Bean', 'Bitter_Gourd', 'Bottle_Gourd', 'Brinjal', 'Broccoli', 'Cabbage', 'Capsicum', 'Carrot', 'Cauliflower', 'Cucumber', 'Papaya', 'Potato', 'Pumpkin', 'Radish', 'Tomato']
#loading the model
device = "gpu" if torch.cuda.is_available() else "cpu"
model = VeggieNet(num_classes=len(class_names))
model.load_state_dict(torch.load("veggie_net.pth", map_location=device))
model.eval()
#image preprocessing
transform = transforms.Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# prediction function
def predict(image):
img = image.convert("RGB")
img = transform(img)
img = img.unsqueeze(0)
with torch.no_grad():
outputs = model(img)
_, predicted = torch.max(outputs, 1)
return class_names[predicted.item()]
# gradio ui
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs="label",
title="🥕 Vegetable Image Classifier",
description="Upload a vegetable image and the model will try to guess what it is! The model will guess the below vegetables: 'Bean', 'Bitter Gourd', 'Bottle Gourd', 'Brinjal', 'Broccoli', 'Cabbage', 'Capsicum', 'Carrot', 'Cauliflower', 'Cucumber', 'Papaya', 'Potato', 'Pumpkin', 'Radish', 'Tomato'"
)
#launching the app
if __name__ == "__main__":
interface.launch() |