uploaded 8 files

alter.py

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+import streamlit as st
+import tensorflow as tf
+from tensorflow import keras
+from PIL import Image
+import numpy as np
+#from process import process
+
+
+model = tf.keras.models.load_model(r'C:\Users\Souvik Chand\Documents\python_my\apps\stream lits\cats and dogs\model3.h5')
+labels = ['Cat', 'Dog']
+
+def preprocess_image(image):
+    image = image.resize((256, 256))
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    return image
+
+def process(image):
+    image= tf.cast(image/255, tf.float32)
+    return image
+
+st.title('Dogs and cats')
+st.write('Upload an image and the model will predict its class.')
+
+st.sidebar.title('upload a photo')
+uploaded_file = st.sidebar.file_uploader('choose image',accept_multiple_files=False, type=['jpg'])
+
+if uploaded_file is not None:
+  image = Image.open(uploaded_file)
+  
+  st.image(image, caption='Uploaded Image', use_column_width=True,width=100)
+  st.write(f'Original Image Shape: {image.size}')
+  #image = image.resize((256, 256))  # Adjust based on your model's input size
+  #image = np.array(image) / 255.0   # Normalize the image
+  #image = np.expand_dims(image, axis=0)
+  preprocessed_image = preprocess_image(image)
+  test_input = preprocessed_image.reshape((1,256,256,3))
+
+  predictions = model.predict(test_input)[0][0]
+  confidence= round((abs(predictions-0.5)/0.5)*100)
+  st.write(predictions)
+
+  if predictions<0.2:
+     st.write(f'Predicted class: Cat')
+  elif predictions>0.8:
+     st.write('Prediction class: Dog')
+  elif (predictions <0.7) or (predictions >0.6):
+     st.write('i feel you are trying to trick me!')
+  else:
+     st.write("looks like neither")
+
+  st.write(f'confidence: {confidence}%')
+
+  #predicted_class = np.argmax(predictions)
+  #confidence = predictions[0][predicted_class]
+    
+  #st.write(f'Predicted class: {predicted_class[0]}')
+  #st.write(f'Predicted Class: {labels[predicted_class]}')
+  #st.write(f'Confidence: {confidence:.2%}')

app.py

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+import streamlit as st
+import tensorflow as tf
+from tensorflow import keras
+from PIL import Image
+import numpy as np
+
+
+#model
+model = tf.keras.models.load_model(r'C:\Users\Souvik Chand\Documents\python_my\apps\stream lits\cats and dogs\model3.h5')
+labels = ['Cat', 'Dog']
+
+def preprocess_image(image):
+    """resizes the image"""
+    image = image.resize((256, 256))      # Adjust based on your model's input size
+    image = np.array(image) / 255.0       # Normalize the image
+    image = np.expand_dims(image, axis=0)
+    return image
+
+def process(image):
+    """not in use"""
+    image= tf.cast(image/255, tf.float32)
+    return image
+
+st.title('Dogs and cats')
+st.write('Upload an image and the model will predict its class.')
+
+st.sidebar.title('upload a photo')
+uploaded_file = st.sidebar.file_uploader('choose image',accept_multiple_files=False, type=['jpg'])
+
+if uploaded_file is not None:
+  image = Image.open(uploaded_file)
+  
+  st.image(image, caption='Uploaded Image', use_column_width=True,width=100)
+  st.write(f'Original Image Shape: {image.size}')
+  
+  preprocessed_image = preprocess_image(image)
+  test_input = preprocessed_image.reshape((1,256,256,3))
+
+  predictions = model.predict(test_input)[0][0]
+  confidence= round((abs(predictions-0.5)/0.5)*100)
+  st.write(predictions)
+
+  if predictions<0.2:
+     st.write(f'Predicted class: Cat')
+  elif predictions>0.8:
+     st.write('Prediction class: Dog')
+  elif (predictions <0.7) or (predictions >0.6):
+     st.write('i feel you are trying to trick me!')
+  else:
+     st.write("looks like neither")
+
+  st.write(f'confidence: {confidence}%')
+
+  

config.toml

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+[client]
+showErrorDetails = false
+
+[theme]
+primaryColor = "#AAA421"
+backgroundColor = "blue"

info.txt

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+set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+streamli run app.py --server.enableXsrfProtection false

instruction.txt

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+streamlit run app.py --server.enableXsrfProtection false

model32.json

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+{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"module": "keras.layers", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 256, 256, 3], "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "batch_input_shape": [null, 256, 256, 3], "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 256, 256, 3]}}, {"module": "keras.layers", "class_name": "BatchNormalization", "config": {"name": "batch_normalization", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "gamma_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "moving_mean_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "moving_variance_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 254, 254, 32]}}, {"module": "keras.layers", "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}, "registered_name": null, "build_config": {"input_shape": [null, 254, 254, 32]}}, {"module": "keras.layers", "class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 127, 127, 32]}}, {"module": "keras.layers", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_1", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "gamma_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "moving_mean_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "moving_variance_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 125, 125, 64]}}, {"module": "keras.layers", "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}, "registered_name": null, "build_config": {"input_shape": [null, 125, 125, 64]}}, {"module": "keras.layers", "class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 62, 62, 64]}}, {"module": "keras.layers", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_2", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "gamma_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "moving_mean_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "moving_variance_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 60, 60, 128]}}, {"module": "keras.layers", "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}, "registered_name": null, "build_config": {"input_shape": [null, 60, 60, 128]}}, {"module": "keras.layers", "class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "registered_name": null, "build_config": {"input_shape": [null, 30, 30, 128]}}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 115200]}}, {"module": "keras.layers", "class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.1, "noise_shape": null, "seed": null}, "registered_name": null, "build_config": {"input_shape": [null, 128]}}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 128]}}, {"module": "keras.layers", "class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.1, "noise_shape": null, "seed": null}, "registered_name": null, "build_config": {"input_shape": [null, 64]}}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 64]}}]}, "keras_version": "2.15.0", "backend": "tensorflow"}

process.py

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+import tensorflow as tf
+
+def process(image):
+    image= tf.cast(image/255, tf.float32)
+    return image

requirements.txt

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+tensorflow==2.11.0
+keras==2.11.0
+streamlt==1.35.0
+pillow=9.3.0
+numpy==1.24.3
+protobuf==3.20.0
+toml==0.10.2
+python==3.10.0