Upload 3 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+static_cnn_colab_final.keras filter=lfs diff=lfs merge=lfs -text

app1.py

@@ -0,0 +1,29 @@
+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+
+model = load_model("static_cnn_colab_final.keras")
+
+class_names = ['cloudy', 'desert', 'green_area', 'water']
+
+def predict_image(img):
+    img = img.resize((128,128))
+    img_array = np.array(img)/255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    predictions = model.predict(img_array)
+    predicted_class = class_names[np.argmax(predictions)]
+    confidence = np.max(predictions)
+
+    return f"{predicted_class} ({confidence*100:.2f}%)"
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Satellite Image Classifier",
+    description="Upload a satellite image and the model will predict its class (cloudy, desert, green_area, water)."
+)
+
+iface.launch(share=False)

requirements.txt

@@ -0,0 +1,6 @@
+numpy
+pandas
+matplotlib
+seaborn
+tensorflow
+keras

static_cnn_colab_final.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e7871fb47c237ba9f6d55b3ae76a47ebfe8d12374e77322f6e85918c78ceadae
+size 78258434