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Weather.py

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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()

requirements.txt

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+torch
+transformers
+gradio
+Pillow
+requests