app.py

import torch
import torchvision.transforms as transforms
from PIL import Image
import gradio as gr
from model import create_model

image_height, image_width = 224, 224

# Load checkpoint
checkpoint = torch.load("flower_checkpoint.pth", map_location="cpu")
model = create_model(num_classes=len(checkpoint["classes"]), dropout=checkpoint["dropout"])
model.load_state_dict(checkpoint["model_state"])
model.eval()
classes = checkpoint["classes"]  # Make sure classes are defined

# Transform
transform = transforms.Compose([
    transforms.Resize((image_height, image_width)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406],
                         [0.229, 0.224, 0.225])
])

# Prediction function
def predict(image):
    # Ensure PIL Image
    if not isinstance(image, Image.Image):
        image = Image.fromarray(image).convert("RGB")
    else:
        image = image.convert("RGB")
    
    image = transform(image).unsqueeze(0)  # add batch dimension
    with torch.no_grad():
        outputs = model(image)
        probs = torch.nn.functional.softmax(outputs, dim=1)
        confidence, predicted = torch.max(probs, 1)
    
    # Return all class probabilities
    return {classes[i]: float(probs[0][i]) for i in range(len(classes))}

# Gradio app
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),  # use PIL to avoid numpy issues
    outputs=gr.Label(num_top_classes=5),
    title="Flower Classification",
    description="Upload a flower image to classify."
)

if __name__ == "__main__":
    demo.launch()