app.py

app.py

@@ -11,13 +11,15 @@ import gradio as gr
 # Load the openAI's CLIP model
 #model, preprocess = clip.load("ViT-B/32", jit=False)
 #display output photo
-def show_output_image(matched_images) :
+def show_output_image(matched_images, photos) :
    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}?w=640"
      #photo_image_url = f"https://unsplash.com/photos/{photo_id}?ixid=2yJhcHBfaWQiOjEyMDd9&fm=jpg"
-     response = requests.get(photo_image_url)
+     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))
      #return img
      image.append(img)
@@ -42,6 +44,9 @@ def image_search(search_text, search_image, option):
   photo_ids = pd.read_csv("./photo_ids.csv")
   photo_ids = list(photo_ids['photo_id'])
   
+  # Photo dataset
+  photos = pd.read_csv("./photos.tsv000", sep="\t", header=0)
+  
   # taking features vectors
   photo_features = np.load("./features.npy")
   
@@ -62,13 +67,13 @@ def image_search(search_text, search_image, option):
     # Find the matched Images
     matched_images = find_matches(text_features, photo_features, photo_ids, 4)
     # ---- debug purpose ------#
-    print(matched_images[0])
-    id = matched_images[0]
-    photo_image_url = f"https://unsplash.com/photos/{id}/download?w=280"
-    print(photo_image_url)
+    #print(matched_images[0])
+    #id = matched_images[0]
+    #photo_image_url = f"https://unsplash.com/photos/{id}/download?w=280"
+    #print(photo_image_url)
     #--------------------------#
   
-    return show_output_image(matched_images)
+    return show_output_image(matched_images, photos)
   elif option == "Image-To-Image":
     # Input Image for Search
     with torch.no_grad():