Create app.py

app.py

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+import PIL
+from torchvision import datasets, transforms, models
+import torch
+from PIL import Image
+import torch.nn as nn 
+import pandas as pd
+import numpy as np 
+import gradio as gr
+
+class_names = ['apple_pie',
+ 'bibimbap',
+ 'cannoli',
+ 'edamame',
+ 'falafel',
+ 'french_toast',
+ 'ramen',
+ 'sushi',
+ 'tiramisu']
+
+def pil_loader(path):
+        with open(path, 'rb') as f:
+            img = Image.open(f)
+            return img.convert('RGB')
+
+def predict(img_path):
+    # Load and preprocess the image
+    # image = pil_loader(img_path)
+        # Convert Gradio image input to a NumPy array
+    img_array = img_path.astype(np.uint8)
+
+    # # Convert NumPy array to PIL Image
+    image = Image.fromarray(img_array)
+
+    test_transforms = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+
+    # Apply transformations
+    image = test_transforms(image)
+
+    inf_model = models.resnet18(pretrained=False)
+    num_ftrs = inf_model.fc.in_features
+    # Here the size of each output sample is set to 2.
+    # Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
+    inf_model.fc = nn.Linear(num_ftrs, len(class_names))
+
+# model_1 = model_1.to(device)
+    inf_model.to(torch.device('cpu'))
+    inf_model.load_state_dict(torch.load('./resnet18_tinyfood_classifier.pth', map_location='cpu'))
+
+    # Perform inference
+    with torch.no_grad():
+        inf_model.eval()
+        out = inf_model(image.unsqueeze(0))  # Add batch dimension
+    
+    # Get the predicted class and confidence
+    _, preds = torch.max(out, 1)
+    idx = preds.cpu().numpy()[0]
+    pred_class = class_names[idx]
+
+    # Assuming `out` is logits, you may need to apply softmax instead of sigmoid
+    probabilities = torch.softmax(out, dim=1)  # Apply softmax to get probabilities
+    confidence = probabilities[0, idx].item() * 100  # Get confidence for the predicted class
+
+    nutrition_data_path = './food-data.csv'
+    # Membaca file CSV
+    df = pd.read_csv(nutrition_data_path)
+
+    # Mencocokkan prediksi dengan data CSV
+    if pred_class.capitalize() in df["Makanan"].values:
+        row = df.loc[df["Makanan"] == pred_class.capitalize()]
+
+        # Mengambil informasi gizi
+        calories = row["Kalori"].values[0]
+        protein = row["Protein"].values[0]
+        fat = row["Lemak"].values[0]
+        carbs = row["Karbohidrat"].values[0]
+        fiber = row["Serat"].values[0]
+        sugar = row["Gula"].values[0]
+        price = row["Harga (Rp)"].values[0]
+
+        return pred_class, calories, protein, fat, carbs, fiber, sugar, price
+    else:
+        nutrition_info = None
+        return 'Food not found', 0, 0, 0, 0, 0, 0
+
+    # return pred_class, confidence
+
+
+# img_path = '/content/drive/MyDrive/Assignment-Citra-SkillacademyAI/bibimbap.jpeg'
+# print(predict(img_path))
+
+interface = gr.Interface(
+    predict,
+    inputs="image",
+    title="Selera Cafe App",
+    description="This App will provide the information of your food choice in Selera Cafe. The menu includes: Apple Pie, Bibimbap, Cannoli, Edamame, Falafel, French Toast, Ramen, Sushi, Tiramisu. Enjoy your food!",
+
+    outputs=[
+      gr.Text(label="Food Label"),
+      gr.Number(label="Calories"),
+      gr.Number(label="Protein"),
+      gr.Number(label="Fat"),
+      gr.Number(label="Carbs"),
+      gr.Number(label="Fiber"),
+      gr.Number(label="Sugar"),
+      gr.Number(label="Price")
+    ],
+    examples = [
+        '/content/drive/MyDrive/Assignment-Citra-SkillacademyAI/bibimbap.jpeg',
+        '/content/drive/MyDrive/Assignment-Citra-SkillacademyAI/food-101-tiny/apple-pie.jpeg',
+        '/content/drive/MyDrive/Assignment-Citra-SkillacademyAI/food-101-tiny/cannoli.jpeg'
+    ])
+interface.launch()