app.py

import gradio as gr
import torch
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
from safetensors.torch import load_model, save_model
from models import *
import os


class WasteClassifier:
    def __init__(self, model, class_names, device):
        self.model = model
        self.class_names = class_names
        self.device = device
        self.transform = transforms.Compose(
            [
                transforms.Resize((384, 384)),
                transforms.ToTensor(),
                transforms.Normalize(
                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
                ),
            ]
        )

    def predict(self, image):
        self.model.eval()

        if not isinstance(image, Image.Image):
            image = Image.fromarray(image)

        original_size = image.size
        img_tensor = self.transform(image).unsqueeze(0).to(self.device)

        with torch.no_grad():
            outputs = self.model(img_tensor)
            probabilities = torch.nn.functional.softmax(outputs, dim=1)

        probs = probabilities[0].cpu().numpy()
        pred_class = self.class_names[np.argmax(probs)]
        confidence = np.max(probs)

        results = {
            "predicted_class": pred_class,
            "confidence": confidence,
            "class_probabilities": {
                class_name: float(prob)
                for class_name, prob in zip(self.class_names, probs)
            },
        }

        return results


def interface(classifier):
    def process_image(image):
        results = classifier.predict(image)

        output_str = f"Predicted Class: {results['predicted_class']}\n"
        output_str += f"Confidence: {results['confidence']*100:.2f}%\n\n"
        output_str += "Class Probabilities:\n"

        sorted_probs = sorted(
            results["class_probabilities"].items(), key=lambda x: x[1], reverse=True
        )

        for class_name, prob in sorted_probs:
            output_str += f"{class_name}: {prob*100:.2f}%\n"

        return output_str

    demo = gr.Interface(
        fn=process_image,
        inputs=[gr.Image(type="pil", label="Upload Image")],
        outputs=[gr.Textbox(label="Classification Results")],
        title="Waste Classification System",
        description="""
        Upload an image of waste to classify it into different categories.
        The model will predict the type of waste and show confidence scores for each category.
        """,
        examples=(
            [["example1.jpg"], ["example2.jpg"], ["example3.jpg"]]
            if os.path.exists("example1.jpg")
            else None
        ),
        analytics_enabled=False,
        theme="default",
    )

    return demo


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class_names = [
    "Cardboard",
    "Food Organics",
    "Glass",
    "Metal",
    "Miscellaneous Trash",
    "Paper",
    "Plastic",
    "Textile Trash",
    "Vegetation",
]
best_model = ResNet50(num_classes=len(class_names))
best_model = best_model.to(device)
load_model(
    best_model,
    os.path.join(os.path.dirname(os.path.abspath(__file__)), "bjf8fp.safetensors"),
)

classifier = WasteClassifier(best_model, class_names, device)

demo = interface(classifier)
demo.launch()