Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.models import load_model
+import numpy as np
+
+# Load the model with weights
+model_path = "https://huggingface.co/spaces/shamimjony1000/shamimjony/blob/main/my_model.h5"  # Replace with the correct path to your model
+model = load_model(model_path)
+
+# Define the image height and width
+image_height = 224
+image_width = 224  # Adjust to match the input size of your model
+
+# Map the class indices to class names based on your dataset
+class_names = {
+    0: "Class 0",
+    1: "Class 1",
+    2: "Class 2",
+    3: "Class 3",
+    4: "Class 4",
+}
+
+# Define the prediction function
+def predict_image(img_array):
+    # Preprocess the input image
+    img_array = img_array.reshape((1, image_height, image_width, 3))  # Reshape to (1, 224, 224, 3)
+    #img_array = img_array.astype(np.float32) / 255.0  # Normalize pixel values
+
+    # Perform additional preprocessing if needed, e.g., center-cropping
+
+    # Make predictions using the loaded model
+    predictions = model.predict(img_array)
+
+    # Get the predicted class label
+    predicted_class_index = np.argmax(predictions)
+    predicted_class = class_names[predicted_class_index]
+
+    # Return the predicted class name for Gradio to display in the output
+    return predicted_class
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs="image",  # Gradio will automatically create an image uploader
+    outputs="text",  # Display the predicted class name as text
+)
+
+# Launch the Gradio Interface
+iface.launch()