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Runtime error
SatwikKambham
commited on
Commit
•
32e703d
1
Parent(s):
073572d
Add application code and requirements
Browse files- README.md +0 -1
- app.py +74 -0
- requirements.txt +5 -0
README.md
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license: apache-2.0
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---
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-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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license: apache-2.0
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---
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app.py
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import gradio as gr
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import numpy as np
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import onnxruntime as ort
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import torchvision as tv
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from huggingface_hub import hf_hub_download
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CATEGORIES = [
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"agricultural",
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"airplane",
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"baseballdiamond",
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"beach",
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"buildings",
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"chaparral",
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"denseresidential",
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"forest",
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"freeway",
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"golfcourse",
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"harbor",
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"intersection",
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"mediumresidential",
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"mobilehomepark",
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"overpass",
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"parkinglot",
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"river",
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"runway",
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"sparseresidential",
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"storagetanks",
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"tenniscourt",
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]
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class Classifier:
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def __init__(self, model_path):
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self.model_path = model_path
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self.session = ort.InferenceSession(self.model_path)
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self.img_transforms = tv.transforms.Compose(
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[
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tv.transforms.Resize((256, 256)),
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tv.transforms.ToTensor(),
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tv.transforms.Normalize(
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(0.48422758, 0.49005175, 0.45050276),
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(0.17348297, 0.16352356, 0.15547496),
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),
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]
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)
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def predict(self, image):
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inp = self.img_transforms(image).unsqueeze(0).numpy()
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logits = self.session.run(
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None,
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{self.session.get_inputs()[0].name: inp},
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)[0]
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probs = np.exp(logits) / np.sum(np.exp(logits))
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return {
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category: float(prob)
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for category, prob in zip(
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CATEGORIES,
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probs[0],
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)
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}
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model_path = hf_hub_download(
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repo_id="SatwikKambham/land_use_classifier",
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filename="model.onnx",
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)
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classifier = Classifier(model_path)
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interface = gr.Interface(
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fn=classifier.predict,
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inputs=gr.components.Image(label="Input image", type="pil"),
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outputs=gr.components.Label(label="Predicted class", num_top_classes=3),
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)
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interface.launch()
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requirements.txt
ADDED
@@ -0,0 +1,5 @@
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numpy
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onnxruntime
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torch
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torchvision
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huggingface_hub
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