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import gradio as gr
import torch
from torch import nn
from torchvision import models, transforms
from PIL import Image
# Load the model architecture and weights
model_id = "KabeerAmjad/food_classification_model"
model = models.resnet50(pretrained=False) # Do not load the pretrained weights here
model.fc = nn.Linear(model.fc.in_features, 11) # Adjust the number of classes (replace 11 with your number of classes)
model.load_state_dict(torch.load(model_id)) # Load the model weights you uploaded
model.eval()
# Define the same preprocessing used during training
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# Define the prediction function
def classify_image(img):
# Preprocess the image
img = transform(img).unsqueeze(0) # Add batch dimension
# Make prediction
with torch.no_grad():
outputs = model(img)
probs = torch.softmax(outputs, dim=-1)
# Get the label with the highest probability
top_label = model.config.id2label[probs.argmax().item()] # Map to label (use your custom label mapping if needed)
return top_label
# Create the Gradio interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil"),
outputs="text",
title="Food Image Classification",
description="Upload an image to classify if it’s an apple pie, etc."
)
# Launch the app
iface.launch()
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