Spaces:
Sleeping
Sleeping
File size: 2,660 Bytes
dae61a5 |
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 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 29 11:12:02 2026
@author: atulkar
"""
import os
import gradio as gr
from transformers import pipeline
# -----------------------------
# Load Image Classification pipeline (pretrained)
# -----------------------------
# Good general-purpose ImageNet-style classifier
clf = pipeline(
task="image-classification",
model="google/vit-base-patch16-224"
)
# -----------------------------
# Locate example images (works locally + on HF Spaces)
# -----------------------------
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
EXAMPLES_DIR = os.path.join(BASE_DIR, "animal_images")
EXAMPLE_FILES = [
"cat.png",
"frog.png",
"hippo.png",
"jaguar.png",
"sloth.png",
"toucan.png",
"turtle.png",
]
examples = []
missing = []
for fname in EXAMPLE_FILES:
fpath = os.path.join(EXAMPLES_DIR, fname)
if os.path.exists(fpath):
examples.append([fpath])
else:
missing.append(fname)
# -----------------------------
# Prediction function
# -----------------------------
def classify_image(img):
"""
img comes in as a PIL image (because gr.Image(type="pil"))
Returns a dict for gr.Label: {label: confidence}
"""
if img is None:
return {}
preds = clf(img, top_k=3)
return {p["label"]: float(p["score"]) for p in preds}
# -----------------------------
# Build Gradio App
# -----------------------------
with gr.Blocks(title="Animal Image Classifier") as demo:
gr.Markdown("# Animal Image Classifier")
gr.Markdown(
"Upload an animal image (or click an example). "
"This app uses a Hugging Face `image-classification` pipeline."
)
with gr.Row():
with gr.Column(scale=1):
inp = gr.Image(type="pil", label="Input Image")
with gr.Row():
btn = gr.Button("Submit", variant="primary")
clr = gr.Button("Clear")
with gr.Column(scale=1):
out = gr.Label(num_top_classes=3, label="Top Predictions")
btn.click(fn=classify_image, inputs=inp, outputs=out)
clr.click(fn=lambda: (None, {}), inputs=None, outputs=[inp, out])
if examples:
gr.Examples(
examples=examples,
inputs=inp,
label="Examples (from ./animal_images/)",
)
if missing:
gr.Markdown(
"\n\nMake sure the folder is next to `app.py` locally, "
"and uploaded into your Hugging Face Space repo when deploying."
)
# -----------------------------
# Launch
# -----------------------------
if __name__ == "__main__":
demo.launch() |