SivilTaram ChengsongHuang commited on
Commit
dba8743
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1 Parent(s): 2a9df48

Update app.py (#5)

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- Update app.py (ab1022cbde1b0a44b497412f0742751b9ceac830)


Co-authored-by: Chengsong Huang <ChengsongHuang@users.noreply.huggingface.co>

Files changed (1) hide show
  1. app.py +33 -3
app.py CHANGED
@@ -12,6 +12,12 @@ import torch
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  import shutil
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  import os
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  import uuid
 
 
 
 
 
 
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  css = """
@@ -21,7 +27,6 @@ css = """
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  """
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  st.markdown(css, unsafe_allow_html=True)
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-
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  def main():
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  st.title("πŸ’‘ LoraHub")
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  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.")
@@ -105,12 +110,28 @@ Infer the date from context. Q: Today is the second day of the third month of 1
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  txt_input, txt_output, max_inference_step=max_step)
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  st.success("Lorahub learning finished! You got the following recommendation:")
 
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  df = {
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  "modules": [LORA_HUB_NAMES[i] for i in st.session_state["select_names"]],
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  "weights": recommendation.value,
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  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  st.table(df)
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-
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  random_id = uuid.uuid4().hex
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  os.makedirs(f"lora/{random_id}")
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  # copy config file
@@ -126,7 +147,16 @@ Infer the date from context. Q: Today is the second day of the third month of 1
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  file_name=f"lora_{random_id}.zip",
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  mime="application/zip"
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  )
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- st.warning("The page will be refreshed once you click the download button.")
 
 
 
 
 
 
 
 
 
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  import shutil
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  import os
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  import uuid
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+ import json
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+
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+
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+ from google.oauth2 import service_account
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+ import gspread
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+ from google.oauth2.service_account import Credentials
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  css = """
 
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  """
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  st.markdown(css, unsafe_allow_html=True)
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  def main():
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  st.title("πŸ’‘ LoraHub")
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  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.")
 
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  txt_input, txt_output, max_inference_step=max_step)
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  st.success("Lorahub learning finished! You got the following recommendation:")
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+
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  df = {
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  "modules": [LORA_HUB_NAMES[i] for i in st.session_state["select_names"]],
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  "weights": recommendation.value,
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  }
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+
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+
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+
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+ def share():
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+ credentials = service_account.Credentials.from_service_account_info(
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+ json.loads(st.secrets["gcp_service_account"]),
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+ scopes=[
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+ "https://www.googleapis.com/auth/spreadsheets",
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+ ]
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+ )
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+ gsheet_url = st.secrets["private_gsheets_url"]
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+ gc = gspread.authorize(credentials)
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+ sh = gc.open_by_url(gsheet_url)
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+
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+ ws = sh.sheet1
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+ ws.insert_rows([[LORA_HUB_NAMES[i] for i in st.session_state["select_names"]],recommendation.value.tolist(),[]])
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  st.table(df)
 
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  random_id = uuid.uuid4().hex
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  os.makedirs(f"lora/{random_id}")
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  # copy config file
 
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  file_name=f"lora_{random_id}.zip",
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  mime="application/zip"
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  )
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+ with open(f"lora_{random_id}.zip", "rb") as fp:
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+ btn = st.download_button(
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+ label="πŸ“₯ Download the final LoRA Module and share your results",
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+ data=fp,
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+ file_name=f"lora_{random_id}.zip",
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+ mime="application/zip",
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+ on_click=share
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+ )
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+ st.button("πŸ“₯ Share your results",on_click=share)
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+ st.warning("The page will be refreshed once you click the download button. Share results may cost 1-2 mins.")
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