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import gradio as gr
import numpy as np
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")
def classify_weather(image_input):
try:
if isinstance(image_input, np.ndarray):
image = Image.fromarray(image_input.astype("uint8")).convert("RGB")
else:
raise TypeError("Only NumPy array input is supported.")
# preprocess as batch
inputs = processor(images=[image], return_tensors="pt")
# inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_id = logits.argmax(-1).item()
predicted_label = model.config.id2label[predicted_class_id]
# optional: return probabilities for Label(num_top_classes=5)
probs = torch.softmax(logits, dim=-1).squeeze().tolist()
labels = [model.config.id2label[i] for i in range(len(probs))]
output_dict = dict(zip(labels, probs))
return output_dict
except Exception as e:
return {"Error": str(e)}
# Gradio interface
iface = gr.Interface(
fn=classify_weather,
inputs=gr.Image(type="numpy"),
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)."
)
if __name__ == "__main__":
iface.launch(show_error=True)
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