inference pipeline

app.py

@@ -1,8 +1,62 @@
 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
 
-def greet(name):
-    return gr.Image(f"https://maps.googleapis.com/maps/api/staticmap?center=64.777466,-147.489792&zoom=17&size=400x400&maptype=satellite&key={os.environ['GOOGLE_API_KEY']}")
+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()
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="image")
-iface.launch()

requirements.txt

@@ -0,0 +1,3 @@
+torch
+torchvision
+PIL