auth

app.py

@@ -7,10 +7,10 @@ from transformers import AutoConfig, AutoTokenizer, pipeline, AutoModelForCausal
 from langchain_community.document_loaders import DirectoryLoader
 import torch
 import re
-import requests
-from urllib.parse import urlencode
 import transformers
 import spaces
+import requests
+from urllib.parse import urlencode
 
 # Initialize embeddings and ChromaDB
 model_name = "sentence-transformers/all-mpnet-base-v2"
@@ -26,6 +26,7 @@ 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_name,
@@ -67,22 +68,19 @@ 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"
 
-# Global variable to store the access token
-access_token = None
+params = {
+    'client_id': CLIENT_ID,
+    'response_type': 'code',
+    'redirect_uri': REDIRECT_URI,
+    'response_mode': 'query',
+    'scope': 'User.Read',
+    'state': '12345'  # Optional state parameter
+}
 
-# OAuth Authorization URL with parameters
-def get_auth_url():
-    params = {
-        'client_id': CLIENT_ID,
-        'response_type': 'code',
-        'redirect_uri': REDIRECT_URI,
-        'response_mode': 'query',
-        'scope': 'User.Read',
-        'state': '12345'  # Optional state parameter
-    }
-    return f"{AUTH_URL}?{urlencode(params)}"
+# Redirect the user to Microsoft's OAuth endpoint
+login_url = f"{AUTH_URL}?{urlencode(params)}"
+print("Redirect to:", login_url)
 
-# Exchange authorization code for an access token
 def exchange_code_for_token(auth_code):
     data = {
         'grant_type': 'authorization_code',
@@ -91,22 +89,12 @@ def exchange_code_for_token(auth_code):
         'code': auth_code,
         'redirect_uri': REDIRECT_URI
     }
+    
     response = requests.post(TOKEN_URL, data=data)
     token_data = response.json()
+    
     return token_data.get('access_token')
 
-# Function to fetch user profile from Microsoft Graph
-def get_user_profile(token):
-    headers = {
-        'Authorization': f'Bearer {token}'
-    }
-    response = requests.get(GRAPH_API_URL, headers=headers)
-    return response.json()
-
-# Function to check if the user is authenticated
-def is_authenticated():
-    return access_token is not None
-
 # Function to retrieve answer using the RAG system
 @spaces.GPU(duration=60)
 def test_rag(query):
@@ -121,60 +109,31 @@ def test_rag(query):
         corrected_text_books = "No helpful answer found."
     
     return corrected_text_books
+
+# Define the Gradio interface
+def chat(query, history=None):
+    if history is None:
+        history = []
+    if query:
+        answer = test_rag(query)
+        history.append((query, answer))
+    return history, ""  # Clear input after submission
+
+# Function to clear input text
+def clear_input():
+    return "",  # Return empty string to clear input field
+
+# Gradio interface
+with gr.Blocks() as interface:
+    gr.Markdown("## RAG Chatbot")
+    gr.Markdown("Ask a question and get answers based on retrieved documents.")
     
-# Gradio app with OAuth integration
-def chat_interface():
-    global access_token
-
-    # If the user is not authenticated, redirect to Microsoft login
-    if not is_authenticated():
-        auth_url = get_auth_url()
-        return gr.Markdown(f"Please [log in]({auth_url}) to use the chatbot.")
-    
-    # Gradio chatbot interface
-    def chat(query, history=None):
-        if history is None:
-            history = []
-        if query:
-            # Chatbot logic here
-            answer = test_rag(query)
-            history.append((query, answer))
-        return history, ""  # Clear input after submission
-
-    with gr.Blocks() as interface:
-        gr.Markdown("## RAG Chatbot")
-        gr.Markdown("Ask a question and get answers based on retrieved documents.")
-        
-        input_box = gr.Textbox(label="Enter your question", placeholder="Type your question here...")
-        submit_btn = gr.Button("Submit")
-        chat_history = gr.Chatbot(label="Chat History")
-        
-        submit_btn.click(chat, inputs=[input_box, chat_history], outputs=[chat_history, input_box])
+    input_box = gr.Textbox(label="Enter your question", placeholder="Type your question here...")
+    submit_btn = gr.Button("Submit")
+    # clear_btn = gr.Button("Clear")
+    chat_history = gr.Chatbot(label="Chat History")
     
-    return interface
-
-# Function to handle OAuth callback
-def handle_auth_callback(auth_code):
-    global access_token
-
-    # Exchange authorization code for access token
-    access_token = exchange_code_for_token(auth_code)
-    return "Authentication successful. You can now use the chatbot."
-
-# Gradio app launch
-with gr.Blocks() as app:
-    gr.Markdown("## OAuth2.0 Chatbot")
-
-    # Add an input field to manually input the authorization code for testing
-    auth_code_input = gr.Textbox(label="Enter the OAuth Authorization Code")
-
-    # Button to handle authentication and exchange the code for the access token
-    auth_button = gr.Button("Authenticate")
-
-    # Callback for authentication
-    auth_button.click(fn=handle_auth_callback, inputs=auth_code_input, outputs="text")
-
-    # Display the chat interface or authentication prompt
-    chat_interface()
+    submit_btn.click(chat, inputs=[input_box, chat_history], outputs=[chat_history, input_box])
+    # clear_btn.click(clear_input, outputs=input_box)
 
-app.launch()
+interface.launch()