Add application code and requirements

README.md

@@ -10,4 +10,3 @@ pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,74 @@
+import gradio as gr
+import numpy as np
+import onnxruntime as ort
+import torchvision as tv
+from huggingface_hub import hf_hub_download
+
+CATEGORIES = [
+    "agricultural",
+    "airplane",
+    "baseballdiamond",
+    "beach",
+    "buildings",
+    "chaparral",
+    "denseresidential",
+    "forest",
+    "freeway",
+    "golfcourse",
+    "harbor",
+    "intersection",
+    "mediumresidential",
+    "mobilehomepark",
+    "overpass",
+    "parkinglot",
+    "river",
+    "runway",
+    "sparseresidential",
+    "storagetanks",
+    "tenniscourt",
+]
+
+
+class Classifier:
+    def __init__(self, model_path):
+        self.model_path = model_path
+        self.session = ort.InferenceSession(self.model_path)
+
+        self.img_transforms = tv.transforms.Compose(
+            [
+                tv.transforms.Resize((256, 256)),
+                tv.transforms.ToTensor(),
+                tv.transforms.Normalize(
+                    (0.48422758, 0.49005175, 0.45050276),
+                    (0.17348297, 0.16352356, 0.15547496),
+                ),
+            ]
+        )
+
+    def predict(self, image):
+        inp = self.img_transforms(image).unsqueeze(0).numpy()
+        logits = self.session.run(
+            None,
+            {self.session.get_inputs()[0].name: inp},
+        )[0]
+        probs = np.exp(logits) / np.sum(np.exp(logits))
+        return {
+            category: float(prob)
+            for category, prob in zip(
+                CATEGORIES,
+                probs[0],
+            )
+        }
+
+
+model_path = hf_hub_download(
+    repo_id="SatwikKambham/land_use_classifier",
+    filename="model.onnx",
+)
+classifier = Classifier(model_path)
+interface = gr.Interface(
+    fn=classifier.predict,
+    inputs=gr.components.Image(label="Input image", type="pil"),
+    outputs=gr.components.Label(label="Predicted class", num_top_classes=3),
+)
+interface.launch()

requirements.txt

@@ -0,0 +1,5 @@
+numpy
+onnxruntime
+torch
+torchvision
+huggingface_hub