app.py

import os
import torch
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image
import json
import gradio as gr
import requests

# Path to the model file and Hugging Face URL
model_path = 'food_classification_model.pth'
model_url = "https://huggingface.co/KabeerAmjad/food_classification_model/resolve/main/food_classification_model.pth"

# Download the model file if it's not already available
if not os.path.exists(model_path):
    print(f"Downloading the model from {model_url}...")
    response = requests.get(model_url)
    with open(model_path, 'wb') as f:
        f.write(response.content)
    print("Model downloaded successfully.")

# Load the model with updated weights parameter
model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
model.eval()  # Set model to evaluation mode

# Load the model's custom state_dict
try:
    state_dict = torch.load(model_path, map_location=torch.device('cpu'))
    model.load_state_dict(state_dict)
    print("Model loaded successfully.")
except RuntimeError as e:
    print("Error loading state_dict:", e)
    print("Ensure that the saved model architecture matches ResNet50.")

# Define the image transformations
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225],
    ),
])

# Load labels
try:
    with open("config.json") as f:
        labels = json.load(f)
    print("Labels loaded successfully.")
except Exception as e:
    print("Error loading labels:", e)

# Function to predict image class
def predict(image):
    try:
        print("Starting prediction...")
        
        # Convert the uploaded file to a PIL image
        input_image = image.convert("RGB")
        print(f"Image converted to RGB: {input_image.size}")
        
        # Preprocess the image
        input_tensor = preprocess(input_image)
        input_batch = input_tensor.unsqueeze(0)  # Add batch dimension
        print(f"Input tensor shape after unsqueeze: {input_batch.shape}")
        
        # Check if a GPU is available and move the input and model to GPU
        if torch.cuda.is_available():
            input_batch = input_batch.to('cuda')
            model.to('cuda')
            print("Using GPU for inference.")
        else:
            print("GPU not available, using CPU.")
        
        # Perform inference
        with torch.no_grad():
            output = model(input_batch)
        print(f"Inference output shape: {output.shape}")
        
        # Get the predicted class with the highest score
        _, predicted_idx = torch.max(output, 1)
        predicted_class = labels[str(predicted_idx.item())]
        print(f"Predicted class index: {predicted_idx.item()}")
        print(f"Predicted class: {predicted_class}")

        return f"Predicted class: {predicted_class}"
    
    except Exception as e:
        print(f"Error during prediction: {e}")
        return f"An error occurred during prediction: {e}"

# Set up the Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Food Classification Model",
    description="Upload an image of food to classify it."
)

# Launch the Gradio app
iface.launch()