try zerospace

app.py

@@ -5,8 +5,8 @@ import datetime
 from huggingface_hub import hf_api, CommitScheduler, HfApi
 from pathlib import Path
 
-# deckify_private = "ByMatthew/deckify_private"
-deckify_private = "eth-zurich-cle/deckify_private"
+deckify_private = "ByMatthew/deckify_private"
+# deckify_private = "eth-zurich-cle/deckify_private"
 
 feedback_file = Path("output_data/") / f"output_{uuid.uuid4()}.json"
 feedback_folder = feedback_file.parent
@@ -87,7 +87,9 @@ def func(file, number_of_pages, secret):
     print(f"Client: {client}")
 
 
+    # output, parsed_document = client.predict(file, number_of_pages)
     output = client.predict(file, number_of_pages)
+    
     if "Error" in output:
         return output   
     # generate a random sequence of numbers
@@ -130,16 +132,21 @@ description = r"""
 3️⃣ <b>Enter the password provided in the invite email.</b><br>
 4️⃣ <b>Click the Generate button</b>: Initiate the slide generation process by clicking the designated "Generate" button.<br>
 5️⃣ <b>Be patient 🙂</b>: Generating the slides could take between 1 minute and 5 minutes.<br>
- 
+6️⃣ <b>Download the slides</b>: Once the slides are generated, you can download them by clicking the "Copy" button.<br>
+7️⃣ <b>Feedback</b>: Please fill out the following [[Feedback Form]](https://docs.google.com/forms/d/e/1FAIpQLScFVZJeNSa9L4t8z5B8whzoLvlNpb95bQdroIPID7aNdv0i4w/viewform?fbzx=-3656849655817576014) <br>
+
+📝 If you have any other issues or questions, please do not hesitate to contact us at ..... 🤗 <br>
 
-🖼️ Some examples of slides generated using <b>SCIDECK</b> are shown below: <br>
-1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift [[Paper]](https://arxiv.org/pdf/1502.03167.pdf) [[Slides]](https://drive.google.com/file/d/1Zt5FFH0nKxut-LyEr9pNAIdtgR_lBtIj/view?usp=sharing) <br>
-2. Attention Is All You Need [[Paper]](https://arxiv.org/pdf/1706.03762.pdf) [[Slides]](https://drive.google.com/file/d/1xKgohh_QKV9pD_XjDuXR566h0VJ1S7WI/view?usp=sharing) <br>
-3. Denoising Diffusion Probabilistic Models [[Paper]](https://arxiv.org/pdf/2006.11239.pdf) [[Slides]](https://drive.google.com/file/d/1D2ZfoJpHR3kP0JdsYyjxUq-vjVMV-KTO/view?usp=sharing) <br>
+Disclaimer: The uploaded files along with the generated outputs will be stored in order to evaluate and improve the service. <br>
 
+Note: If the background process is not running, it may take up to 3 min for it to start. <br>
 
 ver 0.1
 """
+# 🖼️ Some examples of slides generated using <b>SCIDECK</b> are shown below: <br>
+# 1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift [[Paper]](https://arxiv.org/pdf/1502.03167.pdf) [[Slides]](https://drive.google.com/file/d/1Zt5FFH0nKxut-LyEr9pNAIdtgR_lBtIj/view?usp=sharing) <br>
+# 2. Attention Is All You Need [[Paper]](https://arxiv.org/pdf/1706.03762.pdf) [[Slides]](https://drive.google.com/file/d/1xKgohh_QKV9pD_XjDuXR566h0VJ1S7WI/view?usp=sharing) <br>
+# 3. Denoising Diffusion Probabilistic Models [[Paper]](https://arxiv.org/pdf/2006.11239.pdf) [[Slides]](https://drive.google.com/file/d/1D2ZfoJpHR3kP0JdsYyjxUq-vjVMV-KTO/view?usp=sharing) <br>
 
 read_key = os.environ.get("HF_TOKEN", None)