Update app.py

app.py

@@ -1,21 +1,33 @@
 import streamlit as st
 import torch
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from pydrive.auth import GoogleAuth
+from pydrive.drive import GoogleDrive
 import os
+from io import BytesIO
 from zipfile import ZipFile
 
-# Streamlit UI for uploading model
-st.title("Text Summarizer")
+# Initialize Google Auth and Drive
+gauth = GoogleAuth()
+gauth.LocalWebserverAuth()  # Authenticates and opens a browser window
+drive = GoogleDrive(gauth)
 
-uploaded_file = st.file_uploader("Upload the saved model.zip file", type="zip")
+# Streamlit UI
+st.title("Text Summarizer")
 
-if uploaded_file is not None:
-    # Extract the uploaded zip file
-    with ZipFile(uploaded_file, 'r') as zip_ref:
-        zip_ref.extractall("model_directory")
+# Enter the file ID of your model.zip on Google Drive
+model_file_id = st.text_input("Enter the Google Drive file ID of the model.zip")
 
-    # Load the model from the extracted directory
+if model_file_id:
     try:
+        # Download the file from Google Drive
+        downloaded = drive.CreateFile({'id': model_file_id}).GetContentString()
+        
+        # Load the model from the downloaded zip file
+        with ZipFile(BytesIO(downloaded.encode()), 'r') as zip_ref:
+            zip_ref.extractall("model_directory")
+
+        # Load the model from the extracted directory
         model_path = "model_directory"
         tokenizer = AutoTokenizer.from_pretrained(model_path)
         model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
@@ -46,4 +58,4 @@ if text:
             st.write(output)
 
     except Exception as e:
-        st.error(f"An error occurred during summarization: {e}")
+        st.error(f"An error occurred during summarization: {e}")