app.py

import torch
import torch.nn as nn
from torchvision import models, transforms
import gradio as gr
from PIL import Image

# Define the model architecture (must match the saved model)
class_names = ["cordana", "healthy", "pestalotiopsis", "sigatoka"]
def load_model():
    model = models.alexnet(pretrained=False)
    num_ftrs = model.classifier[6].in_features
    model.classifier[6] = nn.Linear(num_ftrs, len(class_names))  # Adjust this for your number of classes

    # Load the model weights
    model.load_state_dict(torch.load('model_alexnet.pth'))
    model.eval()  # Set to evaluation mode

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    model.to(device)
    return model, device

model_alexnet, device = load_model()

# Image transformations
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Prediction function
def predict_image(image):
    image = Image.fromarray(image.astype('uint8'), 'RGB')
    image = transform(image).unsqueeze(0)  # Add batch dimension
    image = image.to(device)
    
    with torch.no_grad():
        outputs = model_alexnet(image)
        _, predicted = torch.max(outputs, 1)
        predicted = predicted.cpu().numpy()
    
    return class_names[predicted[0]]  # Adjust this if needed

    
iface = gr.Interface(fn=predict_image, inputs="image", outputs="label",
                     description="This model is a fine-tuned version of AlexNet specifically designed to identify four types of diseases in banana tree leaves. It can classify the leaves as Cordana, Healthy, Pestalotiopsis, or Sigatoka. Upload a photo of a banana leaf and the model will help you determine its health condition.",
                     examples=[
        'data/test/cordana/1.jpeg',
        'data/test/healthy/5.jpeg',
        'data/test/pestalotiopsis/5.jpeg',
        'data/test/sigatoka/1.jpeg'
    ]
)

iface.launch()