Add Gradio app, requirements, and model

app.py

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+import gradio as gr
+import torch
+from torchvision import transforms, models
+from torch import nn
+from PIL import Image
+
+# Load the model architecture
+model = models.resnet50(weights=None)
+num_classes = 30
+num_features = model.fc.in_features
+model.fc = nn.Linear(num_features, num_classes)
+
+# Load the trained model weights
+try:
+    model.load_state_dict(torch.load("best.pt", map_location=torch.device('cpu')))
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"Error loading model: {e}")
+
+# Load your trained model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+model.eval()
+
+# Define the image transformations (adjust as needed for your model)
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Define class labels
+class_labels = [
+    "aerosol_cans", "aluminum_food_cans", "aluminum_soda_cans", "cardboard_boxes", 
+    "cardboard_packaging", "clothing", "coffee_grounds", "disposable_plastic_cutlery", 
+    "eggshells", "food_waste", "glass_beverage_bottles", "glass_cosmetic_containers", 
+    "glass_food_jars", "magazines", "newspaper", "office_paper", "paper_cups", 
+    "plastic_cup_lids", "plastic_detergent_bottles", "plastic_food_containers", 
+    "plastic_shopping_bags", "plastic_soda_bottles", "plastic_straws", "plastic_trash_bags", 
+    "plastic_water_bottles", "shoes", "steel_food_cans", "styrofoam_cups", 
+    "styrofoam_food_containers", "tea_bags"
+]
+
+# Prediction function
+def predict_image(image):
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+    input_tensor = transform(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        _, predicted = torch.max(outputs, 1)
+        label = class_labels[predicted.item()]
+    return label
+
+# Gradio interface setup
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil", label="Upload Image or Use Webcam"),
+    outputs="text",
+    live=True
+)
+
+# Launch Gradio app
+interface.launch()
+    

requirements.txt

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+gradio==5.6.0
+torch
+torchvision