Create app.py

app.py

@@ -0,0 +1,85 @@
+import streamlit as st
+import requests
+import base64
+import json
+import numpy as np
+from PIL import Image
+
+def get_prediction_img(image_data):
+  url = 'https://askai.aiclub.world/39a4f3a3-e637-4981-a88c-b2597ab12be0'
+  r = requests.post(url, data=image_data)
+  response = r.json()['predicted_label']
+  print("Image AI predicts:",response)
+  return response
+
+def get_prediction_data(data,url):
+#  url = 'https://d3yowc8vr7.execute-api.us-east-1.amazonaws.com/Predict/13d5ab46-b369-4c84-966e-41a0c3ed83d1'
+#  url = 'https://askai.aiclub.world/bc1fe184-efe3-4683-81f4-ededffb6c287'
+  r = requests.post(url, data=json.dumps(data))
+  response = getattr(r,'_content').decode("utf-8")
+  print("Data AI predicts:",response)
+  return response
+
+def processFile(f,url):
+  print("Got file upload")
+  bytesData=f.getvalue()
+  st.image(f)
+  image=Image.open(f)
+  img_array=np.array(image)
+  grayscale_image=convert_grayscale(img_array)
+  final_image=flatten_784(grayscale_image)
+  print("Final image",final_image)
+  prediction=get_prediction_data(final_image,url)
+  print("\n\nData prediction",prediction)
+  predicted_label = json.loads(json.loads(prediction)['body'])['predicted_label']
+  print("\n\nPredicted label", predicted_label)
+  st.title("Data AI says:"+str(predicted_label))
+
+  payload = base64.b64encode(bytesData)
+  response = get_prediction_img(payload)
+  print("\n\nResponse is:",response)
+  st.title("IMAGE AI says:"+response)
+
+def convert_grayscale(im):
+  # Convert to grayscale if its a color image
+  if len(im.shape) > 2 and im.shape[2]>2:
+    red = im[:,:,0]
+    green = im[:,:,1]
+    blue = im[:,:,2]
+    # Convert color to grayscale
+    grayscale_image = (red * 0.299) + (green * 0.587) + (blue * 0.114)
+  elif len(im.shape) == 2:
+    grayscale_image = im
+  return grayscale_image
+
+# This is a helper function to flatten image into a single row after downsampling the image to 28x28
+def flatten_784(grayscale_image):
+  # Find the width and length of the image
+  num_rows_image = grayscale_image.shape[0]
+  num_cols_image = grayscale_image.shape[1]
+  # Figure out the downsampling value for each dimension
+  downsample_rows = int(np.floor(num_rows_image/28))
+  downsample_cols = int(np.floor(num_cols_image/28))
+
+  # Downsample it
+  downsampled_image = grayscale_image[::downsample_rows,::downsample_cols]
+  # Somtimes, the dimensions after downsampling are not accurate, pick the first 28 pixels in each direction
+  downsampled_image = downsampled_image[0:28,0:28]
+  # Convert the vector to a list
+  list_image = list(downsampled_image.reshape(784,))
+  #From the list, create a dictionary
+  e=0
+  d={}
+  for i in range(1,29):
+    for j in range(1,29):
+      l=f"{i}x{j}"
+      d[l]=e
+      e=e+1
+  return d
+
+urlDefault = 'https://askai.aiclub.world/bc1fe184-efe3-4683-81f4-ededffb6c286'
+st.title("Image AI for Gateway")
+url=st.text_input("URL",urlDefault)
+uploadedFile=st.file_uploader("Choose file")
+if uploadedFile is not None:
+  processFile(uploadedFile,url)