Weather.py

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()