app.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from PIL import Image
import gradio as gr
import os

class DeeperCNN(nn.Module):
    def __init__(self, num_classes):
        super(DeeperCNN, self).__init__()
        
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2),
            nn.Dropout(0.25),

            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2),
            nn.Dropout(0.25),

            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Dropout(0.25),
        )

        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(128 * 32 * 32, 256),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(256, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x)
        return x

class_names = ["Eucalyptus globulus", "Pinus pinaster", "Quercus suber"]

model_aliases = {
    "model.pth": "GBIF CNN Model",
    "resnet_model.pth": "ResNet Model"
}

transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor()
])

model = None
num_classes = 3

def load_model(selected_model):
    global model
    model_path = os.path.join("models", selected_model)
    model_path = os.path.normpath(model_path)
    try:
        model = torch.load(model_path, map_location=torch.device("cpu"), weights_only=False)
        model.eval()
        alias = model_aliases.get(selected_model, selected_model)
        return f"{alias} loaded successfully."
    except FileNotFoundError:
        return f"Error: Model file not found at {model_path}"
    except Exception as e:
        return f"Error loading {model_aliases.get(selected_model, selected_model)}: {str(e)}"

def predict(image_path):
    if model is None:
        return {}, "Please load a model first."
    
    image = Image.open(image_path).convert("RGB")
    image = transform(image).unsqueeze(0)
    
    with torch.no_grad():
        outputs = model(image)
        probs = F.softmax(outputs, dim=1)[0]

        probs_dict = {class_names[i]: float(probs[i]) for i in range(len(class_names))}
        sorted_probs = sorted(probs_dict.items(), key=lambda x: x[1], reverse=True)
        top1_class, top1_prob = sorted_probs[0]
        top2_class, top2_prob = sorted_probs[1]

        if top1_prob < 0.6 and (top1_prob - top2_prob) < 0.2:
            final_label = "Inconclusive, low confidence"
        else:
            final_label = top1_class

        return probs_dict, final_label

model_selector = gr.Dropdown(
    choices=[(alias, filename) for filename, alias in model_aliases.items()],
    label="Select Model",
    value="resnet_model.pth"
)

load_button = gr.Button("Load Model")
image_input = gr.Image(type="filepath", interactive=True, label="Input Image", sources=["upload", "clipboard"], height=400)

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=1):
            model_selector.render()
            load_button.render()
            load_output = gr.Textbox(label="Model Status", interactive=False)

            gr.Examples(
                examples=[os.path.join("examples", f) for f in os.listdir("examples") if f.lower().endswith((".jpg", ".jpeg", ".png"))],
                inputs=image_input,
                label="Example Images",
                examples_per_page=9,
                elem_id="custom-example-gallery"
            )
        
        with gr.Column(scale=1):
            image_input.render()
            predict_button = gr.Button("Predict")
        
        with gr.Column(scale=1):
            label_output = gr.Label(label="Prediction Probabilities")
            final_output = gr.Textbox(label="Final Prediction", interactive=False)

    gr.HTML("""
    <style>
    #custom-example-gallery img {
        width: 150px !important;
        height: 150px !important;
        object-fit: cover !important;
    }
    </style>
    """)

    load_button.click(fn=load_model, inputs=model_selector, outputs=load_output)
    predict_button.click(fn=predict, inputs=image_input, outputs=[label_output, final_output])

demo.launch()