Upload app.py

app.py

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+import gradio as gr
+from fastai.vision.all import *
+from huggingface_hub import from_pretrained_fastai
+
+repo_id = "kurianbenoy/paddy_convnext_model"
+learn = from_pretrained_fastai(repo_id)
+labels = learn.dls.vocab
+
+def predict(img):
+    img = PILImage.create(img)
+    _pred, _pred_w_idx, probs = learn.predict(img)
+    # gradio doesn't support tensors, so converting to float
+    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
+    return labels_probs
+
+interface_options = {
+    "title": "Paddy Doctor",
+    "description": "Paddy cultivation requires consistent supervision because several diseases and pests might affect the paddy crops, leading to up to 70% yield loss. This spaces is an online demo to showcase a model build for [real-world Kaggle competition](https://www.kaggle.com/competitions/paddy-disease-classification/overview).",
+    "interpretation": "default",
+    "layout": "horizontal",
+    # Audio from validation file
+    "examples": [
+        "100098.jpg",
+    ],
+    "allow_flagging": "never",
+}
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(shape=(480, 480)),
+    outputs=gr.outputs.Label(num_top_classes=3),
+    **interface_options,
+)
+
+launch_options = {
+    "enable_queue": True,
+    "share": False,
+}
+
+demo.launch(**launch_options)