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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")
# ----------------------
# Inference function
# ----------------------
def classify_weather(image_input):
# Only NumPy array supported for Gradio input
if isinstance(image_input, np.ndarray):
image = Image.fromarray(image_input.astype('uint8')).convert("RGB")
else:
raise TypeError("Only NumPy array input is supported for this Gradio interface.")
# Preprocess
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]
return predicted_label
# ----------------------
# Gradio interface
# ----------------------
iface = gr.Interface(
fn=classify_weather,
inputs=gr.Image(type="numpy"), # NumPy array input
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 app
if __name__ == "__main__":
iface.launch()
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