Update app.py

app.py

@@ -20,20 +20,113 @@ class_names = ['apple_pie',
  '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()]
+
+#         # Convert int64 values to native Python data types
+#         calories = int(row["Kalori"].values[0])
+#         protein = int(row["Protein"].values[0])
+#         fat = int(row["Lemak"].values[0])
+#         carbs = int(row["Karbohidrat"].values[0])
+#         fiber = int(row["Serat"].values[0])
+#         sugar = int(row["Gula"].values[0])
+#         price = int(row["Harga (Rp)"].values[0])
+
+#         # # 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))
+
+
 def pil_loader(path):
+  # Define a function to load an image using PIL and convert it to RGB format
+    with open(path, 'rb') as f:
         with open(path, 'rb') as f:
             img = Image.open(f)
             return img.convert('RGB')
 
 def predict(img_path):
     # Load and preprocess the image
+    ##### Uncomment: without gradio
     # image = pil_loader(img_path)
-        # Convert Gradio image input to a NumPy array
+    
+    ##### Uncomment: with gradio
+    # Convert Gradio image input to a NumPy array
     img_array = img_path.astype(np.uint8)
 
-    # # Convert NumPy array to PIL Image
+    # Convert NumPy array to PIL Image
     image = Image.fromarray(img_array)
 
+    # Define transformations to apply to the image
     test_transforms = transforms.Compose([
         transforms.Resize(256),
         transforms.CenterCrop(224),
@@ -44,87 +137,112 @@ def predict(img_path):
     # Apply transformations
     image = test_transforms(image)
 
+    # Load a pre-trained ResNet18 model and modify the fully connected layer
     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)
+    # model_1 = model_1.to(device)
+
+    # Move the model to CPU and load its state dict from the specified path
     inf_model.to(torch.device('cpu'))
-    inf_model.load_state_dict(torch.load('./resnet18_tinyfood_classifier.pth', map_location='cpu'))
+    inf_model.load_state_dict(torch.load('/content/drive/MyDrive/Assignment-Citra-SkillacademyAI/resnet18_tinyfood_classifier.pth', map_location='cpu'))
 
-    # Perform inference
+    # Perform inference on the image using the model
     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
+    # Apply softmax to get probabilities and calculate confidence for the predicted class
     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
+    # Read nutrition data from a CSV file and match the predicted class
+    nutrition_data_path = '/content/drive/MyDrive/Assignment-Citra-SkillacademyAI/food-data.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()]
 
-        # Convert int64 values to native Python data types
-        calories = int(row["Kalori"].values[0])
-        protein = int(row["Protein"].values[0])
-        fat = int(row["Lemak"].values[0])
-        carbs = int(row["Karbohidrat"].values[0])
-        fiber = int(row["Serat"].values[0])
-        sugar = int(row["Gula"].values[0])
-        price = int(row["Harga (Rp)"].values[0])
-
-        # # 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]
+        # Extract nutrition information for the predicted class
+        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
+        # return "Food", 1, 1, 1, 1, 1, 1
+        # return calories
     else:
         nutrition_info = None
+        # Return 'Food not found' message if predicted class not in the nutrition data
         return 'Food not found', 0, 0, 0, 0, 0, 0
+        # return "Text2"
 
     # return pred_class, confidence
 
 
 # img_path = '/content/drive/MyDrive/Assignment-Citra-SkillacademyAI/bibimbap.jpeg'
+# img_path = '/content/drive/MyDrive/Assignment-Citra-SkillacademyAI/food-101-tiny/cannoli.jpeg'
 # print(predict(img_path))
+        
+
+# interface = gr.Interface(
+#     predict,
+#     inputs="image",
+#     title="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 = [
+#         './bibimbap.jpeg',
+#         './apple-pie.jpeg',
+#         './cannoli.jpeg'
+#     ])
+# interface.launch()
 
 interface = gr.Interface(
     predict,
     inputs="image",
-    title="Cafe App",
+    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(),
     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")
+      gr.Text(label="Calories"),
+      gr.Text(label="Protein"),
+      gr.Text(label="Fat"),
+      gr.Text(label="Carbs"),
+      gr.Text(label="Fiber"),
+      gr.Text(label="Sugar"),
+      gr.Text(label="Price")
     ],
+
     examples = [
-        './bibimbap.jpeg',
-        './apple-pie.jpeg',
-        './cannoli.jpeg'
+        '/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()
+interface.launch()
+
+# Type is not JSON serializable: numpy.int64