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()