app.py

app.py

@@ -28,13 +28,13 @@ photos = pd.read_csv("./photos.tsv000", sep="\t", header=0)
 # taking features vectors
 photo_features = np.load("./features.npy")
 
-def show_output_image(matched_images, photos) :
+def show_output_image(matched_images) :
    image=[]
    for photo_id in matched_images:
      #photo_image_url = f"https://unsplash.com/photos/{photo_id}/download?w=280"
      #photo_image_url = f"https://unsplash.com/photos/{photo_id}?w=640"
      #photo_image_url = f"https://unsplash.com/photos/{photo_id}?ixid=2yJhcHBfaWQiOjEyMDd9&fm=jpg"
-     photo_found = photos[photos["photo_id"] == photo_id].iloc[0]
+     #photo_found = photos[photos["photo_id"] == photo_id].iloc[0]
      #response = requests.get(photo_found["photo_image_url"] + "?w=640")
      #response = requests.get(photo_image_url)
      #img = Image.open(BytesIO(response.content))
@@ -80,7 +80,7 @@ def image_search(search_text, search_image, option):
     #print(photo_image_url)
     #--------------------------#
   
-    return show_output_image(matched_images, photos)
+    return show_output_image(matched_images)
   elif option == "Image-To-Image":
     # Input Image for Search
     with torch.no_grad():
@@ -89,7 +89,7 @@ def image_search(search_text, search_image, option):
       # Find the matched Images
       matched_images = find_matches(image_feature, photo_features, photo_ids, 4)
       #is_input_image = True
-      return show_output_image(matched_images, photos)
+      return show_output_image(matched_images)
   
 gr.Interface(fn=image_search, 
             inputs=[gr.inputs.Textbox(lines=7, label="Input Text"),