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app (2).py

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+import gradio as gr
+import torch
+from torchvision import transforms
+from PIL import Image
+import joblib
+
+# Load model and metadata
+model = torch.jit.load("resnet_grocery_model_scripted.pt", map_location=torch.device("cpu"))
+model.eval()
+
+assets = joblib.load("deployment_assets.joblib")
+transform = assets['transform']
+class_names = assets['class_names']
+
+# Price list
+items = ['Bisconni Chocolate Chip Cookies 46.8gm', 'Coca Cola Can 250ml', 'Colgate Maximum Cavity Protection 75gm',
+         'Fanta 500ml', 'Fresher Guava Nectar 500ml', 'Fruita Vitals Red Grapes 200ml', 'Islamabad Tea 238gm',
+         'Kolson Slanty Jalapeno 18gm', 'Kurkure Chutney Chaska 62gm', 'LU Candi Biscuit 60gm', 'LU Oreo Biscuit 19gm',
+         'LU Prince Biscuit 55.2gm', 'Lays Masala 34gm', 'Lays Wavy Mexican Chili 34gm', 'Lifebuoy Total Protect Soap 96gm',
+         'Lipton Yellow Label Tea 95gm', 'Meezan Ultra Rich Tea 190gm', 'Peek Freans Sooper Biscuit 13.2gm',
+         'Safeguard Bar Soap Pure White 175gm', 'Shezan Apple 250ml', 'Sunsilk Shampoo Soft - Smooth 160ml',
+         'Super Crisp BBQ 30gm', 'Supreme Tea 95gm', 'Tapal Danedar 95gm', 'Vaseline Healthy White Lotion 100ml']
+
+prices = [55.20, 31.75, 90.00, 63.50, 50.00, 35.00, 150.00, 15.00, 25.00, 30.00, 10.00, 30.00, 20.00, 20.00, 44.50, 100.00,
+          200.00, 10.00, 70.00, 25.00, 120.00, 15.00, 100.00, 100.00, 120.00]
+
+pricelist = dict(zip(items, prices))
+
+# Add image to state list
+def add_image(new_img, image_list):
+    if new_img:
+        image_list.append(new_img)
+    return image_list, image_list
+
+# Classify all images and return budget status
+def classify_and_track(images, budget):
+    results = []
+    total_cost = 0
+
+    for img in images:
+        if isinstance(img, Image.Image):
+            input_tensor = transform(img).unsqueeze(0)
+            with torch.no_grad():
+                outputs = model(input_tensor)
+                predicted_index = torch.argmax(outputs, dim=1).item()
+                predicted_class = class_names[predicted_index]
+                price = pricelist.get(predicted_class, 0)
+                total_cost += price
+                results.append((img, f"{predicted_class} - ₱{price:.2f}"))
+        else:
+            results.append((None, "Invalid image"))
+
+    # Budget logic
+    if total_cost <= budget:
+        budget_status = f"🟢 Within budget! Total = ₱{total_cost:.2f} / ₱{budget:.2f}"
+    else:
+        budget_status = f"🔴 Over budget! Total = ₱{total_cost:.2f} / ₱{budget:.2f}"
+
+    return results, budget_status
+
+# Gradio App
+with gr.Blocks(title="Grocery Item Classifier") as app:
+    gr.Markdown("## 🛒 Grocery Classifier with Budget Tracker")
+
+    image_input = gr.Image(type="pil", label="Drop or upload one image")
+    image_list_state = gr.State([])
+
+    add_btn = gr.Button("➕ Add Image")
+    gallery_output = gr.Gallery(label="🖼️ Uploaded Images", columns=3)
+
+    budget_input = gr.Number(label="💰 Your Budget (₱)", value=500.0, precision=2)
+    classify_btn = gr.Button("✅ Classify & Track")
+    budget_output = gr.Textbox(label="📊 Budget Status")
+
+    add_btn.click(
+        fn=add_image,
+        inputs=[image_input, image_list_state],
+        outputs=[image_list_state, gallery_output]
+    )
+
+    classify_btn.click(
+        fn=classify_and_track,
+        inputs=[image_list_state, budget_input],
+        outputs=[gallery_output, budget_output]
+    )
+
+app.launch()

deployment_assets.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c63520922d21bebbb1936d928998d62a99a169d094d59e4a0815caa7f7c2704b
+size 1678

requirements (1).txt

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+gradio
+torch
+torchvision
+joblib
+Pillow

resnet_grocery_model_scripted.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e70f385e2a24a4c62514f88a9ead2580743340a1355c28bbfdf89c2a5a76743
+size 44892341