Upload app.py

app.py

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+import cv2
+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+import json
+
+# Load the saved models
+cnn_model = load_model('cnn_image_classifier.h5')
+resnet_model = load_model('resnet_image_classifier.h5')
+
+# Load label map (assuming you have it defined or loaded elsewhere)
+# Replace with your actual loading method
+with open('EuroSAT/label_map.json', 'r') as f:
+    label_map = json.load(f)
+label_map_inv = {v: k for k, v in label_map.items()}
+
+
+def predict_image(image):
+    # Preprocess the image (resize, normalize, etc.)
+    image = cv2.resize(image, (64, 64))
+    image = image / 255.0
+    image = np.expand_dims(image, axis=0)
+
+    # Make predictions with both models
+    cnn_pred = cnn_model.predict(image)[0]
+    resnet_pred = resnet_model.predict(image)[0]
+
+    # Get top 5 predictions for CNN
+    cnn_top5_indices = np.argsort(cnn_pred)[::-1][:5]
+    cnn_top5 = {
+        label_map_inv[idx]: float(cnn_pred[idx]) for idx in cnn_top5_indices
+    }
+
+    # Get top 5 predictions for ResNet
+    resnet_top5_indices = np.argsort(resnet_pred)[::-1][:5]
+    resnet_top5 = {
+        label_map_inv[idx]: float(resnet_pred[idx]) for idx in resnet_top5_indices
+    }
+
+    # Final predictions
+    cnn_final_prediction = label_map_inv[np.argmax(cnn_pred)]
+    resnet_final_prediction = label_map_inv[np.argmax(resnet_pred)]
+
+    return cnn_top5, cnn_final_prediction, resnet_top5, resnet_final_prediction
+
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=[
+        gr.Label(num_top_classes=5, label="CNN Top 5 Predictions"),
+        gr.Textbox(label="CNN Final Prediction"),
+        gr.Label(num_top_classes=5, label="ResNet Top 5 Predictions"),
+        gr.Textbox(label="ResNet Final Prediction"),
+    ],
+    title="Image Classification with CNN and ResNet",
+    description="Upload an image to classify using two different models.",
+)
+
+iface.launch(debug=True)