app.py

import gradio as gr
import torch
from transformers import AutoFeatureExtractor
from torchvision import models, transforms
from PIL import Image

# Load your trained model from Hugging Face (if available) or load locally
model_id = "KabeerAmjad/food_classification_model"  # Replace with your actual model ID
model = models.resnet50()  # Load ResNet50 architecture
model.load_state_dict(torch.load("path_to_trained_model_weights.pth"))  # Load the trained weights
model.eval()  # Set to evaluation mode

# Load the feature extractor (can be used if any custom preprocessing was applied)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)

# Define the prediction function
def classify_image(img):
    # Preprocess the image to match ResNet50's expected input format
    preprocess = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    img_tensor = preprocess(img).unsqueeze(0)  # Add batch dimension

    # Make prediction with the model
    with torch.no_grad():
        outputs = model(img_tensor)
        probs = torch.softmax(outputs, dim=1)

    # Get the label with the highest probability
    _, predicted_class = torch.max(probs, 1)

    # If you have a list of class labels, use it
    class_labels = ["Apple Pie", "Burger", "Pizza", "Tacos"]  # Replace with your actual class labels
    predicted_label = class_labels[predicted_class.item()]

    return predicted_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, burger, pizza, etc."
)

# Launch the app
iface.launch()