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import gradio as gr
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification
# Load model
processor = AutoImageProcessor.from_pretrained("prithivMLmods/Weather-Image-Classification")
model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Weather-Image-Classification")
# Inference function
def classify_weather(image_path):
try:
image = Image.open(image_path).convert("RGB")
inputs = processor(images=[image], return_tensors="pt")
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 dict(zip(labels, probs))
except Exception as e:
return {"Error": str(e)}
# Gradio interface
iface = gr.Interface(
fn=classify_weather,
inputs=gr.Image(type="filepath"), # ✅ File path 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)."
)
if __name__ == "__main__":
iface.launch(show_error=True)
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