Upload app.py

app.py

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+
+import tensorflow
+from tensorflow import keras
+from keras.models import load_model
+model1 = load_model("inception.h5")
+
+img_width, img_height = 180, 180
+class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
+num_classes = len(class_names)
+
+def predict_image(img):
+    img_4d = img.reshape(-1, img_width, img_height, 3)      
+    texts = ["Hey Tolulope, the model predicted: "]
+    prediction = model1.predict(img_4d)[0]
+    return {texts[0] + class_names[i]: float(prediction[i]) for i in range(num_classes)}
+
+
+import gradio as gr
+image = gr.inputs.Image(shape=(img_height, img_width))
+label = gr.outputs.Label(num_top_classes=num_classes)
+details = [
+                ["NAME: OLUMIDE TOLULOPE SAMUEL,"],
+                ["MATRIC NO: HNDCOM/22/037"],
+                ["CLASS: HND2"],
+                ["LEVEL: 400L"],
+                ["DEPARTMENT: COMPUTER SCIENCE"],
+             ]
+
+article = """<b>NAME: OLUMIDE TOLULOPE SAMUEL</b> </br> 
+<b>MATRIC NO: HNDCOM/22/037</b> </br> 
+<b>CLASS: HND2</b> </br> 
+<b>LEVEL: 400L</b> </br> 
+<b>DEPARTMENT: COMPUTER SCIENCE</b> 
+              
+              `To run this program locally, follow these steps;`
+            """
+
+
+gr.Interface(fn=predict_image, inputs=image, outputs=label, 
+             title="A Flower Classification Project using python ",
+             description="A flower classification app built using python and deployed using gradio",
+             article=article,
+             interpretation='default').launch()