Update app.py

app.py

@@ -4,6 +4,33 @@ import os
 import csv
 import time
 
+
+import requests
+from PIL import Image
+from torchvision import transforms
+
+# Download human-readable labels for ImageNet.
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+def predict(inp):
+  inp = transforms.ToTensor()(inp).unsqueeze(0)
+  with torch.no_grad():
+    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
+  return confidences
+
+import gradio as gr
+
+gr.Interface(fn=predict, 
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=3),
+             examples=["lion.jpg", "cheetah.jpg"]).launch()
+
+
+    
+
+
 uploaded_images = {'characters': {}, 'terrain': {}}
 
 def get_image_path(img, name, image_type):
@@ -33,16 +60,16 @@ def display_images_from_csv():
     for row in files_info:
         if row[2] == 'characters':
             img_path = f"data/uploadedImages/{row[2]}/{row[0]}/{row[1]}"
-            st.sidebar.image(img_path, width=100, caption=row[0])
+            #st.sidebar.image(img_path, width=100, caption=row[0])
         else:
             img_path = f"data/uploadedImages/{row[2]}/{row[0]}/{row[1]}"
-            st.image(img_path, width=100, caption=row[0])
+            #st.image(img_path, width=100, caption=row[0])
 
 
 # Rewrite for gradio
-image_type = gr.selectbox('Choose image type:', options=['characters', 'terrain'])
-name = gr.text_input('Enter a name for the image:')
-uploaded_files = gr.file_uploader('Upload image(s)', type=['png', 'jpg'], accept_multiple_files=True)
+#image_type = gr.selectbox('Choose image type:', options=['characters', 'terrain'])
+#name = gr.text_input('Enter a name for the image:')
+#uploaded_files = gr.file_uploader('Upload image(s)', type=['png', 'jpg'], accept_multiple_files=True)
 
 
 
@@ -54,7 +81,7 @@ for uploaded_file in uploaded_files:
         uploaded_images[image_type][name].append(bytes_data)
 
         # Rewrite for gradio
-        gr.image(bytes_data, use_column_width=True)
+        #gr.image(bytes_data, use_column_width=True)
         
         update_csv_file(uploaded_file, name, image_type)
 
@@ -62,15 +89,15 @@ if image_type == 'characters':
     if uploaded_images['characters']:
 
         # Rewrite for gradio
-        gr.sidebar.write('**Characters**')
+       # gr.sidebar.write('**Characters**')
         for name, files in uploaded_images['characters'].items():
             for file in files:
 
                 # Rewrite for gradio
-                gr.sidebar.image(file, width=100, caption=name)
+                #gr.sidebar.image(file, width=100, caption=name)
 else:
     if uploaded_images['terrain']:
-        st.write('**Terrain**')
+        #st.write('**Terrain**')
         row = []
         for name, files in uploaded_images['terrain'].items():
             for file in files:
@@ -78,17 +105,17 @@ else:
                 if len(row) == 3:
 
                     # Rewrite for gradio
-                    gr.image(row, width=100 * 3)
+                    #gr.image(row, width=100 * 3)
                     row = []
         if row:
 
             # Rewrite for gradio
-            gr.image(row, width=100 * len(row)) # Last row, if not complete
+            #gr.image(row, width=100 * len(row)) # Last row, if not complete
 
 while True:
     time.sleep(20)
 
     # Rewrite for gradio
-    gr.empty()
+    #gr.empty()
     
     display_images_from_csv()