Initial Commit

cnn_from_scratch_sigmoid

app.py

@@ -0,0 +1,62 @@
+import os
+import pickle
+import tensorflow as tf
+import gradio as gr
+import numpy as np
+
+
+#Transforming or Reading an input image and making it prediction ready as per our model
+#filename :- input image path as str
+#img_shape :- input image shape except color channel as List i.e here [128,128]
+def transform_image_for_prediction(filename,img_shape=[256,256]):
+  """
+  Reads an image from filename, turns it into a tensor
+  and reshapes it to (img_shape, img_shape, colour_channel).
+  """
+  # Read in target file (an image)
+  img = tf.io.read_file(filename)
+  
+  # Decode the read file (array) into a tensor & ensure 3 colour channels 
+  # (our model is trained on images with 3 colour channels and sometimes images have 4 colour channels)
+  img = tf.image.decode_image(img, channels=3)
+  
+  # Resize the image (to the same size our model was trained on)
+  img = tf.image.resize(img,size=img_shape)
+  
+  #Applying Feature scaling as we done it /255. in our training and test data
+  img = img/255.                 #dot at end of 255 so to convert integer into float value 
+  
+  return img
+
+
+def make_prediction(img):
+    cnn_model = pickle.load(open("E:\\Machine Learning\\Projects\\meWhoObserves\\model.pkl","rb"))
+    
+    img_tf = transform_image_for_prediction(img)
+    pred_prob = cnn_model.predict(tf.expand_dims(img_tf, axis=0)) #adding fake dimension which represnts batchno
+    class_names = ['crocodile', 'logs'] #as define in .ipynb file
+    if len(pred_prob[0]) > 1: # prediction for multi-class
+        pred_class = class_names[pred_prob.argmax()] # if more than one output, take the max
+    else:                #prediction for binary class
+        if (pred_prob[0][0] >=0.60):
+            i=1
+        else:
+            i=0
+        pred_class = class_names[i] # if only one output, round
+    
+    result = f"Prediction of being crocodile: {np.round(((1-pred_prob[0][0])*100),2)}%" + "\n" + f"Prediction of being driftwood: {np.round(((pred_prob[0][0])*100),2)}%"   
+    prediction = f"Image belongs to the {pred_class} class"
+    result_for_slider = {"Crocodile": float(1-pred_prob[0][0]), "driftwood": float(pred_prob[0][0])}
+    
+    return prediction,result,result_for_slider
+
+inputs = gr.Image(type='filepath',label="Feed me some image!")  #interactive=True,
+outputs=[
+    gr.Textbox(label="Croc-O-Net Classified Class:"),
+    gr.Textbox(label="Croc-O-Net Predicting Probabilities"),
+    gr.Label("Slider prob")
+]
+
+app = gr.Interface(fn=make_prediction,inputs=inputs,outputs=outputs,title="Welcome to the Croc-O-Net Classifier 🐊🔍",
+                   article="<p style='text-align: center'><a href='https://medium.com/@p3pioneer22/croc-o-net-the-only-cnn-project-you-need-to-get-a-job-d57b86ac8fac' target='_blank'>Blog post</a></p>")
+app.launch()