Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""shadman_Image_classification.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1DCVsfqR-kOz3CHdB99IT75IHBzUtIf3M
+"""
+
+import tensorflow as tf
+import PIL
+import matplotlib.pyplot as plt
+from tensorflow.keras import layers
+import os
+
+import pathlib
+
+flower_dataset = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
+dataset_path = tf.keras.utils.get_file('flower_photos',origin=flower_dataset,untar=True)
+dataset_path = pathlib.Path(dataset_path)
+
+roses = list(dataset_path.glob('roses/*'))
+daisy = list(dataset_path.glob('daisy/*'))
+print(roses[1])
+
+PIL.Image.open(roses[10])
+PIL.Image.open(daisy[10])
+
+# training the dataset 
+
+training_images = tf.keras.preprocessing.image_dataset_from_directory(
+    dataset_path,
+    subset = "training",
+    validation_split = 0.25,
+    seed = 123,
+    image_size = (180, 180),
+    batch_size = 32
+
+)
+
+# validation of images
+
+
+validation_images = tf.keras.preprocessing.image_dataset_from_directory(
+    dataset_path,
+    subset = "validation",
+    validation_split = 0.25,
+    seed = 123,
+    image_size = (180, 180),
+    batch_size = 32
+
+)
+
+flower_classes = training_images.class_names
+print(flower_classes)
+
+from tensorflow.python.framework.func_graph import flatten
+# if there are 5 classes then
+
+dataset_classes = 5
+
+from tensorflow.keras.models import Sequential
+
+model = Sequential([
+      #rescaling
+      layers.experimental.preprocessing.Rescaling(1./255, input_shape = (180,180,3)),
+      layers.Conv2D(16, 3 , padding='same' ,  activation='relu'),
+      layers.MaxPooling2D(),
+      layers.Conv2D(32, 3, padding ='same' , activation = 'relu'),
+      layers.MaxPooling2D(),
+      layers.Conv2D(64 , 3, padding='same', activation='relu'),
+      layers.MaxPooling2D(),
+      layers.Flatten(),
+      layers.Dense(128, activation='relu'),
+      layers.Dense(dataset_classes, activation='softmax')
+])
+
+model.compile(
+    optimizer='adam',
+    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    metrics=['accuracy'])
+
+mymodel = model.fit(
+    training_images,
+    validation_data=validation_images,
+    epochs=10
+)
+
+def predict_input_image(img):
+  img_4d=img.reshape(-1,180,180,3)
+  prediction=model.predict(img_4d)[0]
+  return {flower_classes[i]: float(prediction[i]) for i in range(5)}
+
+! pip install gradio
+
+import gradio as gr
+def predict_input_image(img):
+  img_4d=img.reshape(-1,180,180,3)
+  prediction=model.predict(img_4d)[0]
+  return {flower_classes[i]: float(prediction[i]) for i in range(5)}
+
+image = gr.inputs.Image(shape=(180,180))
+label = gr.outputs.Label(num_top_classes=5)
+
+gr.Interface(fn=predict_input_image, inputs=image, outputs=label,interpretation='default').launch(debug='True')