Update app.py

app.py

@@ -3,6 +3,9 @@ import os
 from transformers import CLIPProcessor, CLIPTextModel, CLIPModel
 
 import gradio as gr
+import requests
+
+
 
 
 model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
@@ -16,6 +19,13 @@ def compute_text_embeddings(list_of_strings):
     inputs = processor(text=list_of_strings, return_tensors="pt", padding=True)
     return model.get_text_features(**inputs)
     
+def download_img(path):
+    img_data = requests.get(path).content
+    local_path = path.split("/")[-1] + ".jpg"
+    with open(local_path, 'wb') as handler:
+        handler.write(img_data)
+    return local_path
+    
 def predict(query):
     corpus = 'Unsplash'
     n_results=3
@@ -23,7 +33,7 @@ def predict(query):
     text_embeddings = compute_text_embeddings([query]).detach().numpy()
     k = 0 if corpus == 'Unsplash' else 1
     results = np.argsort((embeddings[k]@text_embeddings.T)[:, 0])[-1:-n_results-1:-1]
-    paths = [df[k].iloc[i]['path'] for i in results]
+    paths = [download_img(df[k].iloc[i]['path']) for i in results]
     print(paths)
     return paths