File size: 1,680 Bytes
268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 025dfa3 268f498 |
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 |
import gradio as gr
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification
# ----------------------
# Load model + processor
# ----------------------
processor = AutoImageProcessor.from_pretrained("prithivMLmods/Weather-Image-Classification")
model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Weather-Image-Classification")
# ----------------------
# Inference function
# ----------------------
def classify_weather(image_file):
try:
# Open the uploaded file
image = Image.open(image_file).convert("RGB")
# Preprocess
inputs = processor(images=[image], return_tensors="pt")
# Inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits.squeeze()
probs = torch.softmax(logits, dim=-1).tolist()
labels = [model.config.id2label[i] for i in range(len(probs))]
# Return label -> probability dictionary
return dict(zip(labels, probs))
except Exception as e:
# Safe fallback if something unexpected happens
return {"Error": 1.0}
# ----------------------
# Gradio interface
# ----------------------
iface = gr.Interface(
fn=classify_weather,
inputs=gr.File(file_types=[".jpg", ".png"]), # Accept uploaded files
outputs=gr.Label(num_top_classes=5, label="Weather Condition"),
title="Weather Image Classification",
description="Upload an image to classify the weather condition (sun, rain, snow, fog, or clouds)."
)
# Launch the Space with error reporting
if __name__ == "__main__":
iface.launch(show_error=True)
|