unzip with verbose

app.py

@@ -1,6 +1,7 @@
 import s23_openai_clip
 from s23_openai_clip import make_train_valid_dfs 
 from s23_openai_clip import get_image_embeddings 
+from s23_openai_clip import inference_CLIP
 
 import gradio as gr
 import zipfile
@@ -9,10 +10,27 @@ import zipfile
 # query_text = "dogs on the grass"
 image_path = "./Images"
 captions_path = "."
+data_source = 'flickr8k.zip'
+entry_name = "Images"
+
+
+z = zipfile.ZipFile(data_source)
+entry_info = z.getinfo(entry_name)
+i = z.open(entry_name)
+o = open(image_path, 'w')
+offset = 0
+while True:
+    b = i.read(block_size)
+    offset += len(b)
+    set_percentage(float(offset)/float(entry_info.file_size) * 100.)
+    if b == '':
+        break
+    o.write(b)
+i.close()
+o.close()
+set_attributes_from(entry_info)
 
-
-with zipfile.ZipFile('flickr8k.zip', 'r') as zip_ref:
-    zip_ref.extractall('Images')
+    
     
 _, valid_df = make_train_valid_dfs()
 model, image_embeddings = get_image_embeddings(valid_df, "best.pt")
@@ -20,7 +38,7 @@ model, image_embeddings = get_image_embeddings(valid_df, "best.pt")
 
 
 def greet(query_text):
-    return inference_CLIP2(query_text)
+    return inference_CLIP(query_text)
     
 
 gallery = gr.Gallery(