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
+ResNet50_Model.keras filter=lfs diff=lfs merge=lfs -text

CNN_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dfc5ab39025e7a777a86f15c17aa65808c8d1e2d20eda935d06cf37e1e12ae02
+size 8267880

ResNet50_Model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4f8c560aa2406ec0a1e7df86e9c9edce634f45fc615791fa452841f5b8d4c1d0
+size 283901240

app.py

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+import gradio as gr
+     import cv2
+     import numpy as np
+     from tensorflow.keras.models import load_model
+     
+     # Define the class names
+     class_names = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake']
+     
+     # Function to load the selected model
+     def load_selected_model(model_name):
+         if model_name == "ResNet50_Model.keras":
+             return load_model('ResNet50_Model.keras')
+         elif model_name == "CNN_model.h5":
+             return load_model('CNN_model.h5')
+         else:
+             raise ValueError("Invalid model name")
+     
+     # Function to predict the image
+     def predict_image(image, model_name):
+         model = load_selected_model(model_name)
+         img = cv2.resize(image, (64, 64))
+         img = img / 255.0
+         img = np.expand_dims(img, axis=0)
+     
+         predictions = model.predict(img)[0]
+     
+         predicted_class_index = np.argmax(predictions)
+         predicted_class_name = class_names[predicted_class_index]
+     
+         confidence = predictions[predicted_class_index]
+     
+         # Get the top 3 predictions
+         top_indices = predictions.argsort()[-3:][::-1]
+         top_classes = [class_names[i] for i in top_indices]
+         top_confidences = [predictions[i] for i in top_indices]
+     
+         # Format the output
+         result = "Top Predictions:\n"
+         for i in range(3):
+             result += f"{top_classes[i]} (Confidence: {top_confidences[i]:.2f})\n"
+     
+         return result
+     
+     with gr.Blocks() as demo:
+         gr.Markdown("# Area Prediction Angle")
+     
+         with gr.Row():
+             with gr.Column():
+                 model_selector = gr.Dropdown(choices=["ResNet50_Model.keras", "CNN_model.h5"], label="Select Model")
+                 image_input = gr.Image(type="numpy", label="Upload Image")
+                 predict_button = gr.Button("Predict")
+     
+             image_output = gr.Image(type="numpy", label="Uploaded Image")
+     
+         prediction_output = gr.Textbox(label="Prediction")
+     
+         predict_button.click(fn=predict_image, inputs=[image_input, model_selector], outputs=prediction_output)
+         image_input.change(fn=lambda x: x, inputs=image_input, outputs=image_output)
+     
+     demo.launch()
+