app.py

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+import torch
+from huggingface_hub import from_pretrained_fastai
+from pathlib import Path
+
+examples = ["examples/example_0.png", 
+            "examples/example_1.png", 
+            "examples/example_2.png", 
+            "examples/example_3.png", 
+            "examples/example_4.png"]
+            
+repo_id = "hugginglearners/rice_image_classification"
+path = Path("./")
+
+def get_y(r):
+    return r["label"]
+
+def get_x(r):
+    return path/r["fname"]
+
+learner = from_pretrained_fastai(repo_id)
+labels = learner.dls.vocab
+
+def inference(image):
+    label_predict, _, probs = learner.predict(image)
+    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
+    return labels_probs
+
+gr.Interface(
+    fn=inference,
+    title="Rice Disease Classification",
+    description="Predict which type of rice disease is affecting the leaf: Tungro, Rice Blast, Bacterial Blight, or Healthy Rice Leaf.",
+    inputs=gr.Image(),
+    examples=examples,
+    outputs=gr.Label(num_top_classes=4, label='Prediction'),
+    cache_examples=False,
+    article="Authors: Your Team Name",
+).launch(debug=True, enable_queue=True)