Update app.py

app.py

@@ -13,112 +13,7 @@ import numpy as np
 from PIL import Image
 import torch.nn.functional as F
 
-# transform = transforms.Compose([
-#     transforms.Resize((128, 128)),
-#     transforms.ToTensor(),
-#     #transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-#     transforms.RandomHorizontalFlip(),
-#     transforms.RandomRotation(10),
-#     #transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2)
-# ])
 
-# train_dataset = ImageFolder(root='data/train', transform=transform)
-# train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
-
-# val_dataset = ImageFolder(root='data/val', transform=transform)
-# val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
-
-# test_dataset = ImageFolder(root='data/val', transform=transform)
-# test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
-
-# class WeatherNet(nn.Module):
-#     def __init__(self):
-#         super(WeatherNet, self).__init__()
-#         self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
-#         self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
-#         self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
-#         self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
-#         self.fc1 = nn.Linear(128 * 16 * 16, 512)
-#         self.fc2 = nn.Linear(512, 11)  # 11 classes
-
-#     def forward(self, x):
-#         x = self.pool(torch.relu(self.conv1(x)))
-#         x = self.pool(torch.relu(self.conv2(x)))
-#         x = self.pool(torch.relu(self.conv3(x)))
-#         x = x.view(-1, 128 * 16 * 16)
-#         x = torch.relu(self.fc1(x))
-#         x = self.fc2(x)
-#         return x
-
-# model = WeatherNet()
-
-
-# criterion = nn.CrossEntropyLoss()
-# optimizer = optim.Adam(model.parameters(), lr=0.001)
-
-
-# num_epochs = 10
-# for epoch in range(num_epochs):
-#     model.train()
-#     running_loss = 0.0
-#     for images, labels in train_loader:
-#         optimizer.zero_grad()
-#         outputs = model(images)
-#         loss = criterion(outputs, labels)
-#         loss.backward()
-#         optimizer.step()
-#         running_loss += loss.item()
-#     print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}")
-
-#     model.eval()
-#     correct = 0
-#     total = 0
-#     with torch.no_grad():
-#         for images, labels in val_loader:
-#             outputs = model(images)
-#             _, predicted = torch.max(outputs.data, 1)
-#             total += labels.size(0)
-#             correct += (predicted == labels).sum().item()
-#     print(f'Validation Accuracy: {100 * correct / total:.2f}%')
-# torch.save(model.state_dict(), 'weather_model.pth')
-
-
-# def predict_image(image):
-#     image = Image.fromarray(image).convert("RGB")
-#     image = transform(image)
-#     image = image.unsqueeze(0)  # Add batch dimension
-
-#     with torch.no_grad():
-#         outputs = model(image)
-#         probabilities = F.softmax(outputs, dim=1)
-#         _, predicted = torch.max(outputs, 1)
-
-#     predicted_label = classes[predicted.item()]
-#     predicted_probability = probabilities[0][predicted.item()].item()
-
-#     return predicted_label, predicted_probability
-
-# # Class labels
-# classes = ['dew', 'fog_smog', 'frost', 'glaze', 'hail', 'lightning', 'rain', 'rainbow', 'rime', 'sandstorm', 'snow']
-
-# # Create Gradio interface
-# interface = gr.Interface(fn=predict_image, 
-#                          inputs=gr.components.Image(), 
-#                          outputs=[gr.components.Textbox(label="Predicted Label"), gr.components.Textbox(label="Prediction Probability")],title="Weather Detection App")
-
-# # Launch the interface
-# interface.launch()
-
-import gradio as gr
-import torch
-import torch.nn as nn
-import torchvision.transforms as transforms
-from torchvision.datasets import ImageFolder
-from torch.utils.data import DataLoader
-import torch.nn.functional as F
-from PIL import Image
-
-# Define the transformation
 transform = transforms.Compose([
     transforms.Resize((128, 128)),
     transforms.ToTensor(),
@@ -126,7 +21,7 @@ transform = transforms.Compose([
     transforms.RandomRotation(10),
 ])
 
-# Define the model
+#Remember peak acc was 72%
 class WeatherNet(nn.Module):
     def __init__(self):
         super(WeatherNet, self).__init__()
@@ -146,21 +41,17 @@ class WeatherNet(nn.Module):
         x = self.fc2(x)
         return x
 
-# Initialize the model
 model = WeatherNet()
 
-# Load the saved model weights
 model.load_state_dict(torch.load('weather_model.pth'))
 model.eval()
 
-# Class labels
 classes = ['dew', 'fog_smog', 'frost', 'glaze', 'hail', 'lightning', 'rain', 'rainbow', 'rime', 'sandstorm', 'snow']
 
-# Prediction function
 def predict_image(image):
     image = Image.fromarray(image).convert("RGB")
     image = transform(image)
-    image = image.unsqueeze(0)  # Add batch dimension
+    image = image.unsqueeze(0)
 
     with torch.no_grad():
         outputs = model(image)
@@ -172,7 +63,7 @@ def predict_image(image):
 
     return predicted_label, predicted_probability
 
-# Create Gradio interface
+#Gradio interface
 interface = gr.Interface(
     fn=predict_image,
     inputs=gr.components.Image(),
@@ -180,5 +71,4 @@ interface = gr.Interface(
     title="Weather Detection App"
 )
 
-# Launch the interface
 interface.launch()