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gradioapp.py

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+# -*- coding: utf-8 -*-
+"""gradioapp
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1cD6eAJDAuLfS_1T9LE749-TpglGMtUq8
+"""
+
+!pip install gradio
+
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import gdown
+import os
+
+# Load model
+def load_model():
+    model_path = "resnet50_cifar10_model.h5"
+    if not os.path.exists(model_path):
+        url = "https://drive.google.com/uc?id=13KgM2DddlsscFQx4uoYK0lesSE6-DAo3"
+        gdown.download(url, model_path, quiet=False)
+    model = tf.keras.models.load_model(model_path)
+    return model
+
+model = load_model()
+
+class_names = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer',
+               'Dog', 'Frog', 'Horse', 'Ship', 'Truck']
+
+# Prediction function
+def predict_cifar10(image):
+    image = image.convert("RGB")
+    img = image.resize((32, 32))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)
+    predicted_label = class_names[np.argmax(prediction)]
+    confidence = float(np.max(prediction)) * 100
+
+    return {predicted_label: confidence}
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_cifar10,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="🚀 CIFAR-10 Image Classifier using ResNet50",
+    description="Upload an image, and the model will classify it into one of the 10 CIFAR-10 classes."
+)
+
+iface.launch()
+

requirements.txt

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+gradio
+tensorflow
+numpy
+Pillow
+gdown
+opencv-python