import gradio as gr import tensorflow as tf import gdown from PIL import Image import pillow_avif input_shape = (32, 32, 3) resized_shape = (224, 224, 3) num_classes = 10 labels = { 0: "plane", 1: "car", 2: "bird", 3: "cat", 4: "deer", 5: "dog", 6: "frog", 7: "horse", 8: "ship", 9: "truck", } # Download the model file def download_model(): url = "https://drive.google.com/uc?id=12700bE-pomYKoVQ214VrpBoJ7akXcTpL" output = "modelV2Lmixed.keras" gdown.download(url, output, quiet=False) return output model_file = download_model() # Load the model model = tf.keras.models.load_model(model_file) # Perform image classification for single class output def predict_class(image): img = tf.cast(image, tf.float32) img = tf.image.resize(img, [input_shape[0], input_shape[1]]) img = tf.expand_dims(img, axis=0) prediction = model.predict(img) class_index = tf.argmax(prediction[0]).numpy() predicted_class = labels[class_index] print("predicted_class is ", predicted_class)#################################################### return predicted_class # Perform image classification for multy class output # def predict_class(image): # img = tf.cast(image, tf.float32) # img = tf.image.resize(img, [input_shape[0], input_shape[1]]) # img = tf.expand_dims(img, axis=0) # prediction = model.predict(img) # return prediction[0] # UI Design for single class output def classify_image(image): predicted_class = predict_class(image) output = f"

Predicted Class: {predicted_class}

" return output # UI Design for multy class output # def classify_image(image): # results = predict_class(image) # print(results) # output = {labels.get(i): float(results[i]) for i in range(len(results))} # result = output if max(output.values()) >=0.98 else {"NO_CIFAR10_CLASS": 1} # return result inputs = gr.inputs.Image(type="pil", label="Upload an image") outputs = gr.outputs.HTML() #uncomment for single class output #outputs = gr.outputs.Label(num_top_classes=4) title = "

Image Classifier

" description = "Upload an image and get the predicted class." # css_code='body{background-image:url("file=wave.mp4");}' gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title=title, examples=[["00_plane.jpg"], ["01_car.jpg"], ["02_house.jpg"], ["03_cat.jpg"], ["04_deer.jpg"]], # css=css_code, description=description, enable_queue=True).launch()