Create app.py

app.py

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+import gradio as gr
+
+from keras.preprocessing import image
+from keras.applications.vgg16 import preprocess_input, decode_predictions
+import numpy as np
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from glob import glob
+# loading the directories 
+# importing the libraries
+import tensorflow as tf
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Flatten, Dense
+from tensorflow.keras.applications import VGG16
+#from keras.preprocessing import image
+num_classes=10
+IMAGE_SHAPE = [224, 224]
+
+class_labels = ['exterior_building','icons','interior_building','landscapes','layouts','others','people','scanned_documents','signatures','under_construction']
+
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+
+model = tf.keras.models.load_model("./classification_model.h5")
+class_labels = ['exterior_building','icons','interior_building','landscapes','layouts','others','people','scanned_documents','signatures','under_construction']
+
+def predict_image(image):
+
+    
+    # img_path = '/Users/balamuruga/Desktop/Screenshot 2023-11-08 at 9.22.52 PM.png'
+    # img = image.load_img(img_path, target_size=(224, 224))
+    # x = image.img_to_array(img)
+    # x = np.expand_dims(x, axis=0)
+    # x = preprocess_input(x)
+    image = image.reshape((-1, 224, 224, 3))
+
+
+    # preds=model.predict(image)
+    prediction = model.predict(image).flatten()
+    print(prediction)
+    return {class_labels[i]: float(prediction[i]) for i in range(10)}
+
+    # create a list containing the class labels
+    
+    # # find the index of the class with maximum score
+    # pred = np.argmax(preds, axis=-1)
+    
+    # # print the label of the class with maximum score
+    # print(class_labels[pred[0]])
+    # return {class_labels[i]: float(pred[i]) for i in range(10)}
+    
+  # img_4d=img.reshape(-1,256,256,3)
+  # prediction=model.predict(img_4d)[0]
+  # return {class_names[i]: float(prediction[i]) for i in range(5)}
+
+# iface = gr.Interface(fn=predict_image, inputs="text", outputs="text")
+# iface.launch()
+
+
+image = gr.inputs.Image(shape = (224, 224)) 
+label = gr.outputs.Label(num_top_classes = 10)
+gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch(debug='True')