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a3730f6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import gradio as gr
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification
# Load model and processor
MODEL_NAME = "nexusbert/resnet50-cassava-finetuned"
processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
id2label = model.config.id2label
def predict(image: Image.Image):
"""
Run inference on a single image and return class probabilities.
Gradio's Label component expects a dict: {label: probability}.
"""
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0]
# Map class indices to human-readable labels and probabilities (as floats)
result = {
id2label[i]: float(probs[i])
for i in range(len(probs))
}
return result
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Upload cassava leaf image"),
outputs=gr.Label(num_top_classes=3, label="Predicted classes"),
title="Cassava Leaf Disease Classifier",
description=(
"Upload an image of a cassava leaf and this app will predict the most likely disease class "
"using the `nexusbert/resnet50-cassava-finetuned` model from Hugging Face."
),
examples=None,
)
if __name__ == "__main__":
# Set share=True if you want a public shareable link
demo.launch()
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