Upload 3 files

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.applications.resnet import preprocess_input
+from tensorflow.keras.models import load_model
+
+# Load the model
+model = load_model("best_model.h5")
+
+# Class names
+class_names = ['Cloudy', 'Rain', 'Shine', 'Sunrise']
+
+# Preprocessing function
+def preprocess_image(img):
+    img = img.resize((224, 224))
+    img_array = np.array(img)
+    img_array = preprocess_input(img_array)
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+# Prediction function
+def classify_image(image):
+    processed_img = preprocess_image(image)
+    preds = model.predict(processed_img)[0]
+    predicted_class = class_names[np.argmax(preds)]
+    confidence = float(np.max(preds))
+    return {predicted_class: confidence}
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=4),
+    title="Weather Image Classifier",
+    description="Upload an image of the weather and get the predicted category (Cloudy, Rain, Shine, Sunrise)"
+)
+
+if __name__ == "__main__":
+    interface.launch()

best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1606bc87695d0909aac9d6c4c051a6a0879626f4c650e398378fa25846f29a03
+size 223971152

requirements.txt

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+gradio
+tensorflow
+numpy
+pillow