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| import gradio as gr | |
| import os | |
| import requests | |
| from PIL import Image | |
| from torchvision import transforms | |
| import torch | |
| import torchvision.models as models | |
| import torch.nn as nn | |
| import io | |
| import wandb | |
| run = wandb.init(project="farmnet", job_type='inference') | |
| artifact = run.use_artifact("farmnet_model_1:latest", type='model') | |
| artifact_dir = artifact.download() | |
| wandb.finish() | |
| class FarmNet(nn.Module): | |
| def __init__(self): | |
| super(FarmNet, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 16, 3, padding=1) | |
| self.conv2 = nn.Conv2d(16, 32, 3, padding=1) | |
| self.conv3 = nn.Conv2d(32, 64, 3, padding=1) | |
| self.pool = nn.MaxPool2d(2, 2) | |
| self.fc1 = nn.Linear(64 * 50 * 50, 512) | |
| self.fc2 = nn.Linear(512, 2) | |
| self.relu = nn.ReLU() | |
| def forward(self, x): | |
| x = self.pool(self.relu(self.conv1(x))) | |
| x = self.pool(self.relu(self.conv2(x))) | |
| x = self.pool(self.relu(self.conv3(x))) | |
| x = x.view(-1, 64 * 50 * 50) | |
| x = self.relu(self.fc1(x)) | |
| x = self.fc2(x) | |
| return x | |
| model = FarmNet() # Replace with your model and architecture | |
| model.load_state_dict(torch.load('artifacts/farmnet_model_1:v0/farmnet_model.pth')) | |
| model.eval() # Set the model to evaluation mode | |
| # Preprocess the image | |
| transform = transforms.Compose([ | |
| transforms.Resize((400, 400)), # Adjust according to your model's input size | |
| transforms.ToTensor(), | |
| ]) | |
| # Print the prediction | |
| classes = ['not farm', 'farm'] # Adjust according to your classes | |
| #64.777466,-147.489792 | |
| def greet(latitude,longitude): | |
| image_url = f"https://maps.googleapis.com/maps/api/staticmap?center={latitude},{longitude}&zoom=17&size=400x400&maptype=satellite&key={os.environ['GOOGLE_API_KEY']}" | |
| response = requests.get(image_url) | |
| img_data = response.content | |
| pil_img = Image.open(io.BytesIO(img_data)).convert('RGB') | |
| img = transform(pil_img) | |
| img = img.unsqueeze(0) # Add batch dimension | |
| # Make an inference | |
| with torch.no_grad(): | |
| outputs = model(img) | |
| _, predicted = torch.max(outputs, 1) | |
| return gr.Image(pil_img), classes[predicted.item()] | |
| iface = gr.Interface(fn=greet, inputs=["number","number"], outputs=["image","label"]) | |
| iface.launch() | |