auth update

app.py

@@ -1,15 +1,4 @@
 import subprocess
-
-script_path = './setup.sh'  # Adjust the path if needed
-
-# Run the script
-exit_code = subprocess.call(['bash', script_path])
-
-if exit_code == 0:
-    print("Script executed successfully.")
-else:
-    print(f"Script failed with exit code {exit_code}.")
-
 import gradio as gr
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import Chroma
@@ -19,11 +8,20 @@ from transformers import AutoConfig, AutoTokenizer, pipeline, AutoModelForCausal
 from langchain_community.document_loaders import DirectoryLoader
 import torch
 import re
-import transformers
-import spaces
 import requests
 from urllib.parse import urlencode, urlparse, parse_qs
 
+# Step 1: Run the setup script
+script_path = './setup.sh'  # Adjust the path if needed
+
+# Run the script
+exit_code = subprocess.call(['bash', script_path])
+
+if exit_code == 0:
+    print("Script executed successfully.")
+else:
+    print(f"Script failed with exit code {exit_code}.")
+
 # Initialize embeddings and ChromaDB
 model_name = "sentence-transformers/all-mpnet-base-v2"
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -38,9 +36,8 @@ books_db_client = books_db.as_retriever()
 
 # Initialize the model and tokenizer
 model_name = "stabilityai/stablelm-zephyr-3b"
-
-model_config = transformers.AutoConfig.from_pretrained(model_name, max_new_tokens=1024)
-model = transformers.AutoModelForCausalLM.from_pretrained(
+model_config = AutoConfig.from_pretrained(model_name, max_new_tokens=1024)
+model = AutoModelForCausalLM.from_pretrained(
     model_name,
     trust_remote_code=True,
     config=model_config,
@@ -49,7 +46,7 @@ model = transformers.AutoModelForCausalLM.from_pretrained(
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-query_pipeline = transformers.pipeline(
+query_pipeline = pipeline(
     "text-generation",
     model=model,
     tokenizer=tokenizer,
@@ -77,8 +74,8 @@ TENANT_ID = '2b093ced-2571-463f-bc3e-b4f8bcb427ee'
 CLIENT_ID = '2a7c884c-942d-49e2-9e5d-7a29d8a0d3e5'
 CLIENT_SECRET = 'EOF8Q~kKHCRgx8tnlLM-H8e93ifetxI6x7sU6bGW'
 REDIRECT_URI = 'https://sanjeevbora-chatbot.hf.space/'
-AUTH_URL = f"https://login.microsoftonline.com/2b093ced-2571-463f-bc3e-b4f8bcb427ee/oauth2/v2.0/authorize"
-TOKEN_URL = f"https://login.microsoftonline.com/2b093ced-2571-463f-bc3e-b4f8bcb427ee/oauth2/v2.0/token"
+AUTH_URL = f"https://login.microsoftonline.com/{TENANT_ID}/oauth2/v2.0/authorize"
+TOKEN_URL = f"https://login.microsoftonline.com/{TENANT_ID}/oauth2/v2.0/token"
 
 params = {
     'client_id': CLIENT_ID,
@@ -93,7 +90,7 @@ params = {
 login_url = f"{AUTH_URL}?{urlencode(params)}"
 
 def show_login_button():
-    return f'<a href="{login_url}" class = "GFG"> Click here to login with Microsoft </a>'
+    return f'<a href="{login_url}" class="GFG"> Click here to login with Microsoft </a>'
 
 def exchange_code_for_token(auth_code):
     data = {
@@ -120,11 +117,10 @@ def handle_redirect(url):
 
     if auth_code:
         token = exchange_code_for_token(auth_code[0])
-        return token  # return the token or handle accordingly
+        return token  # Return the token or handle accordingly
     return None
 
-# Function to retrieve answer using the RAG system
-@spaces.GPU(duration=60)
+# Function to retrieve answers using the RAG system
 def test_rag(query):
     books_retriever = books_db_client_retriever.run(query)
     
@@ -146,26 +142,38 @@ def chat(query, history=None):
         history.append((query, answer))
     return history, ""  # Clear input after submission
 
+# Gradio interface
 with gr.Blocks() as interface:
-    gr.Markdown("## RAG Chatbot")
-    gr.Markdown("Please log in to continue.")
-    
-    login_link = gr.HTML(show_login_button())
-    
-    # Components for chat (initially hidden)
-    input_box = gr.Textbox(label="Enter your question", placeholder="Type your question here...", visible=False)
-    submit_btn = gr.Button("Submit", visible=False)
-    chat_history = gr.Chatbot(label="Chat History", visible=False)
-
-    # Add a function to handle the redirect from Microsoft
-    redirect_url_input = gr.Textbox(label="Redirect URL", visible=False)
-
-    redirect_url_input.change(
-        handle_redirect,
-        inputs=[redirect_url_input],
-        outputs=[input_box, submit_btn]  # Update visibility based on login
-    )
-
-    submit_btn.click(chat, inputs=[input_box, chat_history], outputs=[chat_history, input_box])
-
-interface.launch()
+    with gr.Tab("Login"):
+        gr.Markdown("## Login Page")
+        login_link = gr.HTML(show_login_button())
+        
+        # Hidden textbox for redirect URL
+        redirect_url_input = gr.Textbox(label="Redirect URL", visible=False)
+        
+        # Handle redirect
+        redirect_url_input.change(
+            handle_redirect,
+            inputs=[redirect_url_input],
+            outputs=[redirect_url_input],
+            show_progress=True
+        )
+        
+    with gr.Tab("Chatbot"):
+        gr.Markdown("## Chatbot Page")
+        
+        # Components for chat (initially hidden)
+        input_box = gr.Textbox(label="Enter your question", placeholder="Type your question here...", visible=False)
+        submit_btn = gr.Button("Submit", visible=False)
+        chat_history = gr.Chatbot(label="Chat History", visible=False)
+
+        redirect_url_input.change(
+            handle_redirect,
+            inputs=[redirect_url_input],
+            outputs=[input_box, submit_btn, chat_history],  # Update visibility based on login
+            show_progress=True
+        )
+
+        submit_btn.click(chat, inputs=[input_box, chat_history], outputs=[chat_history, input_box])
+
+interface.launch()