Upload app.py

app.py

@@ -4,43 +4,73 @@ import numpy as np
 import gdown
 from PIL import Image
 
+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",
+}
 
-labels = [
-    "plane",
-    "car",
-    "bird",
-    "cat",
-    "deer",
-    "dog",
-    "frog",
-    "horse",
-    "ship",
-    "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
 
-# a file
-url = "https://drive.google.com/uc?id=12700bE-pomYKoVQ214VrpBoJ7akXcTpL"
-output = "modelV2Lmixed.keras"
-gdown.download(url, output, quiet=False)
+model_file = download_model()
 
-inception_net = tf.keras.models.load_model("./modelV2Lmixed.keras")
+# Load the model
+model = tf.keras.models.load_model(model_file)
 
+# Perform image classification
+def predict_class(image):
+    img = tf.cast(image, tf.float32)
+    img = tf.image.resize(img, [input_shape[0], input_shape[1]])
+    img = np.expand_dims(img, axis=0)
+    prediction = model.predict(img)
+    return prediction[0]
 
-def classify_image(inp):
-    inp = inp.reshape((-1, 224, 224, 3))
-    inp = tf.keras.applications.efficientnet.preprocess_input(inp)
-    prediction = inception_net.predict(inp).flatten()
-    confidences = {labels[i]: float(prediction[i]) for i in range(10)}
-    return confidences
+# UI Design
+def classify_image(image):
+    pred = predict_class(image)
+    class_names = [
+        "plane",
+        "car",
+        "bird",
+        "cat",
+        "deer",
+        "dog",
+        "frog",
+        "horse",
+        "ship",
+        "truck",
+    ]
 
+    probabilities = tf.nn.softmax(pred)
+    top_indices = tf.argsort(probabilities, direction='DESCENDING')
+    top_classes = [class_names[idx] for idx in top_indices]
+    top_probs = [probabilities[idx] for idx in top_indices]
 
-import gradio as gr
+    output = "<h3>Top 3 Predictions:</h3>"
+    for i in range(3):
+        output += f"<p>{top_classes[i]}: {top_probs[i]*100:.2f}%</p>"
+
+    return output
+
+inputs = gr.inputs.Image(label="Upload an image")
+outputs = gr.outputs.HTML()
+
+title = "<h1 style='text-align: center;'>Image Classifier</h1>"
+description = "Upload an image and get the top 3 predictions."
 
-gr.Interface(
-    fn=classify_image,
-    inputs=gr.inputs.Image(shape=(32, 32)),
-    outputs=gr.outputs.Label(num_top_classes=3),
-    examples=["03_cat.jpg", "05_dog.jpg"],
-    theme="default",
-    css=".footer{display:none !important}",
-).launch()
+gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title=title, description=description).launch()