Added examples and cleaned requierments

app.py

@@ -112,25 +112,22 @@ def inference(query, class1_name="class1", support_imgs=None, class2_name="class
 title = "P>M>F few-shot learning pipeline"
 description = "Short description: We take a ViT-small backbone, which is pre-trained with DINO, and meta-trained on Meta-Dataset; for few-shot classification, we use a ProtoNet classifier. The demo can be viewed as zero-shot since the support set is built by searching images from Google. Note that you may need to play with GIS parameters to get good support examples. Besides, GIS is not very stable as search requests may fail for many reasons (e.g., number of requests reaches the limit of the day). This code is heavely inspired from the original HF space <a href='https://huggingface.co/spaces/hushell/pmf_with_gis' target='_blank'>here</a>"
 article = "<p style='text-align: center'><a href='http://arxiv.org/abs/2204.07305' target='_blank'>Arxiv</a></p>"
-
-
 gr.Interface(fn=inference,
              inputs=[
                  gr.Image(label="Image to classify", type="pil"),
-                 #gr.Textbox(lines=1, label="Class hypotheses:", placeholder="Enter class names separated by ','",),
-                
                  gr.Textbox(lines=1, label="First class name :", placeholder="Enter first class name",),
-                 gr.File(label="Drag or select one or more photos of the first class", file_types=["image"], file_count="multiple"),
-                 
+                 gr.File(label="First class example images", file_types=["image"], file_count="multiple"),
                  gr.Textbox(lines=1, label="Second class name :", placeholder="Enter second class name",),
-                 gr.File(label="Drag or select one or more photos of the second class", file_types=["image"], file_count="multiple"),
+                 gr.File(label="Second class example iamges", file_types=["image"], file_count="multiple"),
              ],
              theme="grass",
              outputs=[
                  gr.Label(label="Predicted class probabilities"),
-                 #gr.Image(type='pil', label="Support examples from Google image search"),
              ],
              title=title,
              description=description,
              article=article,
+             examples=[
+                 ["./example_images/2007_000033.jpg", "plane", ["./example_images/2007_000738.jpg", "./example_images/2007_000256.jpg"], "cat", ["./example_images/2007_000528.jpg", "./example_images/2007_000549.jpg"]]
+             ]
             ).launch(debug=True)

requirements.txt

@@ -9,6 +9,5 @@ timm
 ml-collections
 ftfy
 tensorboard
-Google-Images-Search
 semantic-version
 pytz