web app

app.py

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+import streamlit as st
+import json
+import requests
+import base64
+from PIL import Image
+import io
+
+def get_prediction(image_data):
+  #replace your image classification ai service URL
+  url = 'https://askai.aiclub.world/9e64ab8b-95e4-40fa-9529-b13d9e1b4761'
+  r = requests.post(url, data=image_data)
+  st.write(r)
+  response = r.json()['predicted_label']
+  score = r.json()['score']
+  #print("Predicted_label: {} and confidence_score: {}".format(response,score))
+  return response, score
+
+
+#creating the web app
+
+#setting up the title
+st.title("Cats and Dogs Image Classifier")#change according to your project
+#setting up the subheader
+st.subheader("File Uploader")#change according to your project
+
+#file uploader
+image = st.file_uploader(label="Upload an image",accept_multiple_files=False, help="Upload an image to classify them")
+if image:
+    #converting the image to bytes
+    img = Image.open(image)
+    buf = io.BytesIO()
+    img.save(buf,format = 'JPEG')
+    byte_im = buf.getvalue()
+
+    #converting bytes to b64encoding
+    payload = base64.b64encode(byte_im)
+
+    #file details
+    file_details = {
+      "file name": image.name,
+      "file type": image.type,
+      "file size": image.size
+    }
+
+    #write file details
+    st.write(file_details)
+
+    #setting up the image
+    st.image(img)
+
+    #predictions
+    response, scores = get_prediction(payload)
+
+    col1, col2 = st.columns(2)
+    with col1:
+      st.metric("Prediction Label",response)
+    with col2:
+      st.metric("Confidence Score", max(scores))
+
+
+