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import gradio as gr
from PIL import Image
from transformers import pipeline
classifier = pipeline("image-classification", model="Docty/mangoes")
def classify_image(img):
if not isinstance(img, Image.Image):
img = Image.fromarray(img)
results = classifier(img)
return {res["label"]: float(res["score"]) for res in results}
theme = gr.themes.Soft(
primary_hue="blue",
secondary_hue="lime",
neutral_hue="slate"
)
with gr.Blocks(theme=theme) as demo:
gr.Markdown("## Mango Image Classifier")
gr.Markdown("Upload an image of a mango to classify it using a fine-tuned model.")
with gr.Row():
image_input = gr.Image(type="pil", label="Upload Mango Image")
label_output = gr.Label(num_top_classes=3, label="Predictions")
classify_btn = gr.Button("Classify Image", variant="primary")
gr.Examples(
examples=[
"0.jpg",
"1.jpg",
"2.jpg",
"3.jpg",
"4.jpg",
"5.jpg",
"6.jpg",
"7.jpg"
],
inputs=image_input,
outputs=label_output,
fn=classify_image,
cache_examples=False # set True if you want cached predictions
)
classify_btn.click(fn=classify_image, inputs=image_input, outputs=label_output)
demo.launch(share=True)
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