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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
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('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()
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