app.py

import torch
from PIL import Image
import torchvision.transforms as transforms
import gradio as gr
import os
import gdown

from model import get_model, CLASS_NAMES

MODEL_PATH = "waste_classifier.pth"

# Download model if not present
if not os.path.exists(MODEL_PATH):
    url = "https://drive.google.com/uc?id=1RDBXrDvQ7B71SU-nUybDzbIpXkzHBStV"
    gdown.download(url, MODEL_PATH, quiet=False)

# Load model
model = get_model()
model.load_state_dict(torch.load(MODEL_PATH, map_location="cpu"))
model.eval()

# Transform
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

import torch.nn.functional as F

def predict(image):
    image = image.convert("RGB")
    img = transform(image).unsqueeze(0)

    with torch.no_grad():
        outputs = model(img)
        probs = F.softmax(outputs, dim=1)
        confidence, predicted = torch.max(probs, 1)

    return f"{CLASS_NAMES[predicted.item()]} ({confidence.item()*100:.2f}%)"

# Gradio UI
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Waste Classifier",
    description="Upload an image to classify waste"
)

interface.launch()