updates:

- Rename redCaps
- naming fix
- allow png

app.py

@@ -22,10 +22,16 @@ def gen_show_caption(sub_prompt=None, cap_prompt = ""):
         )
     
 
-st.title("Image Captioning Demo from Redcaps")
+st.title("Image Captioning Demo from RedCaps")
 st.sidebar.markdown(
     """
-    Image Captioning Model from VirTex trained on Redcaps
+    ### Image Captioning Model from VirTex trained on RedCaps
+    
+    Use this page to caption your own images or try out some of our samples.
+    You can also generate captions as if they are from specific subreddits,
+    as if they start with a particular prompt, or even both.
+    
+    Feel free to share your results on twitter with #redcaps or with a friend.
     """
 )
 
@@ -48,6 +54,15 @@ else:
 
     sample_image = sample_images[0 if select_idx is None else select_idx]
 
+
+    uploaded_image = None
+    with st.sidebar.form("file-uploader-form", clear_on_submit=True):
+        uploaded_file = st.file_uploader("Choose a file")
+        submitted = st.form_submit_button("Submit")
+        if uploaded_file is not None and submitted:
+            uploaded_image = Image.open(io.BytesIO(uploaded_file.getvalue()))
+            select_idx = None # set this to help rewrite the cache
+
     # class OnChange():
     #     def __init__(self, idx):
     #         self.idx = idx
@@ -75,16 +90,12 @@ else:
         value=""
     )
 
-
-    uploaded_image = None
-    with st.sidebar.form("file-uploader-form", clear_on_submit=True):
-        uploaded_file = st.file_uploader("Choose a file")
-        submitted = st.form_submit_button("Submit")
-        if uploaded_file is not None and submitted:
-            uploaded_image = Image.open(io.BytesIO(uploaded_file.getvalue()))
-            select_idx = None # set this to help rewrite the cache
-
     _ = st.sidebar.button("Regenerate Caption")
+    
+#     advanced = st.sidebar.checkbox("Advanced Options")
+    
+#     if advanced:
+#         nuc_size = st.sidebar.slider("")
 
     if uploaded_image is None and submitted:
         st.write("Please select a file to upload")
@@ -100,8 +111,11 @@ else:
         else:
             image = Image.open(image_file)
 
+        image = image.convert("RGB")
+
         st.session_state['image'] = image
 
+
         image_dict = imageLoader.transform(image)
 
         show_image = imageLoader.show_resize(image)

model.py

@@ -22,7 +22,7 @@ SAMPLES_PATH = "./samples/*.jpg"
 
 class ImageLoader():
     def __init__(self):
-        self.transformer = torchvision.transforms.Compose([
+        self.image_transform = torchvision.transforms.Compose([
                                torchvision.transforms.ToTensor(),
                                torchvision.transforms.Resize(256),
                                torchvision.transforms.CenterCrop(224),
@@ -30,7 +30,7 @@ class ImageLoader():
         self.show_size=500
         
     def load(self, im_path):
-        im = torch.FloatTensor(self.transformer(Image.open(im_path))).unsqueeze(0)
+        im = torch.FloatTensor(self.image_transform(Image.open(im_path))).unsqueeze(0)
         return {"image": im}
     
     def raw_load(self, im_path):
@@ -38,7 +38,7 @@ class ImageLoader():
         return {"image": im}
     
     def transform(self, image):
-        im = torch.FloatTensor(self.transformer(image)).unsqueeze(0)
+        im = torch.FloatTensor(self.image_transform(image)).unsqueeze(0)
         return {"image": im}
     
     def text_transform(self, text):

requirements.txt

@@ -14,4 +14,5 @@ torch==1.7.0
 torchvision==0.8
 tqdm>=4.50.0
 wordsegment==1.3.1
+whatimage==0.0.3
 git+git://github.com/facebookresearch/fvcore.git#egg=fvcore