share data

README.md

@@ -8,7 +8,6 @@ sdk_version: 1.24.0
 app_file: app.py
 pinned: false
 license: mit
-python_version: 3.9.13
 ---
 
 The space of arxiv.org/abs/2307.13269.

app.py

@@ -12,12 +12,6 @@ import torch
 import shutil
 import os
 import uuid
-import json
-
-
-from google.oauth2 import service_account
-import gspread
-from google.oauth2.service_account import Credentials
 
 
 css = """
@@ -27,6 +21,7 @@ css = """
 """
 st.markdown(css, unsafe_allow_html=True)
 
+
 def main():
     st.title("💡 LoraHub")
     st.markdown("Low-rank adaptations (LoRA) are techniques for fine-tuning large language models on new tasks. We propose LoraHub, a framework that allows composing multiple LoRA modules trained on different tasks. The goal is to achieve good performance on unseen tasks using just a few examples, without needing extra parameters or training. And we want to build a marketplace where users can share their trained LoRA modules, thereby facilitating the application of these modules to new tasks.")
@@ -110,28 +105,12 @@ Infer the date from context.  Q: Today is the second day of the third month of 1
                                                   txt_input, txt_output, max_inference_step=max_step)
                 
             st.success("Lorahub learning finished! You got the following recommendation:")
-
             df = {
                 "modules": [LORA_HUB_NAMES[i] for i in st.session_state["select_names"]],
                 "weights": recommendation.value,
             }
-
-
-
-            def share():
-                credentials = service_account.Credentials.from_service_account_info(
-                json.loads(st.secrets["gcp_service_account"]),
-                scopes=[
-                    "https://www.googleapis.com/auth/spreadsheets",
-                ]
-                )
-                gsheet_url = st.secrets["private_gsheets_url"]
-                gc = gspread.authorize(credentials)
-                sh = gc.open_by_url(gsheet_url)
-
-                ws = sh.sheet1
-                ws.insert_rows([[LORA_HUB_NAMES[i] for i in st.session_state["select_names"]],recommendation.value.tolist(),[max_step]])
             st.table(df)
+            
             random_id = uuid.uuid4().hex
             os.makedirs(f"lora/{random_id}")
             # copy config file
@@ -147,15 +126,7 @@ Infer the date from context.  Q: Today is the second day of the third month of 1
                     file_name=f"lora_{random_id}.zip",
                     mime="application/zip"
                 )
-            with open(f"lora_{random_id}.zip", "rb") as fp:
-                btn = st.download_button(
-                    label="📥 Download and share your results",
-                    data=fp,
-                    file_name=f"lora_{random_id}.zip",
-                    mime="application/zip",
-                    on_click=share
-                )
-            st.warning("The page will be refreshed once you click the download button. Share results may cost 1-2 mins.")
+            st.warning("The page will be refreshed once you click the download button.")
 
 
 

requirements.txt

@@ -1,6 +1,4 @@
 peft
 transformers
 pandas
-nevergrad
-gspread
-google
+nevergrad