Update app.py

app.py

@@ -4,7 +4,6 @@ from streamlit_option_menu import option_menu
 import os
 import plotly.express as px
 from io import StringIO
-from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain.schema import HumanMessage, SystemMessage, AIMessage
 from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
 from langchain.memory import ConversationBufferWindowMemory
@@ -19,33 +18,30 @@ import pandas as pd
 import numpy as np
 from dotenv import load_dotenv
 import re
-#from auth0.v3.authentication import GetToken
-#from auth0.v3.management import Auth0
+from auth0_component import login_button
 
 warnings.filterwarnings("ignore", category=DeprecationWarning)
 
-client_id = os.getenv("client_id")
-client_secret = os.getenv("client_secret")
-auth_domain = os.getenv("auth_domain")
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+load_dotenv()
+
+# Auth0 credentials
+clientId = os.getenv('client_id')
+domain = os.getenv('auth_domain')
+client_secret = os.getenv('client_secret')
+
+
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+llm = ChatGroq(model="llama-3.1-70b-versatile")
 
-llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro",temperature=0,api_key=GOOGLE_API_KEY,convert_system_message_to_human=True)
 
 # Streamlit page configuration
 st.set_page_config(
     page_title="TraffiTrack", 
-    page_icon="", 
+    page_icon=":mag:", 
     layout="wide", 
-    initial_sidebar_state="expanded", 
+    initial_sidebar_state="expanded",
 )
 
-#token = GetToken(auth_domain)
-#mgmt_api_token = token.client_credentials(
- #   client_id,
-  #  client_secret,
-   # 'dev-u4kd3wndapfdk6dr.us.auth0.com'
-#)
-#auth0 = Auth0(auth_domain, mgmt_api_token['access_token'])
 
 # Initialize session state for messages and banned users
 if 'messages' not in st.session_state:
@@ -97,27 +93,16 @@ def check_for_drug_content(input_text):
     ip_addresses = re.findall(ip_pattern, input_text)
 
     return found_drugs, phone_numbers, ip_addresses
-
 # Sidebar with options
-selected = option_menu(
-    "Main Menu", 
-    ["Home", "Registration","Chat"], 
-    icons=['house', 'person','chat-dots'], 
-    menu_icon="cast", 
-    default_index=0, 
-    orientation="horizontal",
-    styles={
-        "container": {"padding": "5px", "background-color": "#DEF9C4"},
-        "icon": {"color": "#468585", "font-size": "25px"},
-        "nav-link": {
-            "font-size": "16px", 
-            "text-align": "left", 
-            "margin": "0px", 
-            "--hover-color": "#9CDBA6"
-        },
-        "nav-link-selected": {"background-color": "#50B498"},
-    }
-)
+
+st.title("Login")
+email = st.text_input("Enter your email...")
+password = st.text_input("Enter your password...",type='password')
+
+
+
+
+
 
 
 # Function to get a response from the chat model
@@ -230,6 +215,9 @@ def get_chatmodel_response(user_message):
             elif isinstance(message, AIMessage):
                 st_message(message.content, is_user=False)
 
+
+        
+
 def display_home_info():
     # Set background color
     st.markdown(
@@ -268,175 +256,190 @@ def display_home_info():
         """,
         unsafe_allow_html=True
     )
-   # Login and Signup Buttons
-    if "auth_token" not in st.session_state:
-        st.write("#### Please login or sign up to access the application:")
-        col1, col2 = st.columns(2)
-
-        with col1:
-            if st.button("Login"):
-                login()
-        
-        with col2:
-            if st.button("Sign Up"):
-                signup()
-    else:
-        st.success("You're logged in!")
 
-if selected == "Registration":
-    registration()
+user_info = login_button(clientId=clientId, domain=domain)
+if user_info:
+    st.write(f'Hi {user_info["nickname"]}')
+    # Uncomment below if you want to show more user information
+    # st.write(user_info)
+else:
+    st.write("Please log in to continue.")
+
+if st.button("Login / SignUp"):
+    selected = option_menu(
+        "Main Menu", 
+        ["Home","Registration","Chat"], 
+        
+        icons=['house', 'person','chat-dots'], 
+        menu_icon="cast", 
+        default_index=0, 
+        orientation="horizontal",
+        styles={
+            "container": {"padding": "5px", "background-color": "#DEF9C4"},
+            "icon": {"color": "#468585", "font-size": "25px"},
+            "nav-link": {
+                "font-size": "16px", 
+                "text-align": "left", 
+                "margin": "0px", 
+                "--hover-color": "#9CDBA6"
+            },
+            "nav-link-selected": {"background-color": "#50B498"},
+        }
+    )
+    if selected == "Registration":
+        registration()
 
-elif selected == "Home":
-    display_home_info()
+    elif selected == "Home":
+        display_home_info()
 
-elif selected == "Chat":
-    
-    def traffitrack_chatbot():
-        st.title('TraffiTrack 💬')
-
-        # Dropdown to select platform
-        platform = st.selectbox(
-            "Choose a platform", 
-            ["Live 💁‍♀️", "WhatsApp 📱", "Instagram 📸", "Telegram ✉️"], 
-            index=0
-        )
-
-        if platform == "Telegram ✉️":
-            # Hardcoded CSV content
-            csv_content = """sender_name,sender_id,phone_number,message_text
-    Shruti,1580593004,917304814120,But I would prefer blowing a bag of Charlie
-    Shruti,1580593004,917304814120,I want to eat ice cream i am bored
-    Shruti,1580593004,917304814120,He’s heavily into smack
-    Shruti,1580593004,917304814120,There was a bag of snow in the car
-    Shruti,1580593004,917304814120,Did you bring the Mary Jane for the party tonight?
-    Shruti,1580593004,917304814120,Mary Jane
-    Ritika,1065437474,918828000465,I WANT A BAG OF CHARLIE
-    Ritika,1065437474,918828000465,Okayy
-    Preeyaj,6649015430,,Haa bhej cocain thoda
-    Ritika,1065437474,918828000465,Maal chahiye?
-    Preeyaj,6649015430,,Llm
-    Ritika,1065437474,918828000465,Kya kar rahe ho?
-    Ritika,1065437474,918828000465,Hey"""
-
-            # Read the CSV content into a DataFrame
-            messages_df = pd.read_csv(StringIO(csv_content))
-            # Reverse the DataFrame to display messages from first to last
-            for idx, row in messages_df[::-1].iterrows():  # Reverse the DataFrame here
-                sender_name = row['sender_name']
-                message_text = row['message_text']
-                # Display each message with its corresponding sender name
-                st_message(f"{sender_name}: {message_text}", is_user=False, key=f"telegram_message_{idx}")
-
-            if st.button("Analyze 🚨"):
-                # Initialize count and list for drug-related messages
-                drug_count = 0
-                drug_messages = []
-                user_data = {}  # Initialize user data dictionary
-
-                # Analyze each message for drug-related content
-                for idx, row in messages_df.iterrows():
-                    message_text = row['message_text']
+    elif selected == "Chat":
+        
+        def traffitrack_chatbot():
+            st.title('TraffiTrack 💬')
+
+            # Dropdown to select platform
+            platform = st.selectbox(
+                "Choose a platform", 
+                ["Live 💁‍♀️", "WhatsApp 📱", "Instagram 📸", "Telegram ✉️"], 
+                index=0
+            )
+
+            if platform == "Telegram ✉️":
+                # Hardcoded CSV content
+                csv_content = """sender_name,sender_id,phone_number,message_text
+        Shruti,1580593004,917304814120,But I would prefer blowing a bag of Charlie
+        Shruti,1580593004,917304814120,I want to eat ice cream i am bored
+        Shruti,1580593004,917304814120,He’s heavily into smack
+        Shruti,1580593004,917304814120,There was a bag of snow in the car
+        Shruti,1580593004,917304814120,Did you bring the Mary Jane for the party tonight?
+        Shruti,1580593004,917304814120,Mary Jane
+        Ritika,1065437474,918828000465,I WANT A BAG OF CHARLIE
+        Ritika,1065437474,918828000465,Okayy
+        Preeyaj,6649015430,,Haa bhej cocain thoda
+        Ritika,1065437474,918828000465,Maal chahiye?
+        Preeyaj,6649015430,,Llm
+        Ritika,1065437474,918828000465,Kya kar rahe ho?
+        Ritika,1065437474,918828000465,Hey"""
+
+                # Read the CSV content into a DataFrame
+                messages_df = pd.read_csv(StringIO(csv_content))
+                # Reverse the DataFrame to display messages from first to last
+                for idx, row in messages_df[::-1].iterrows():  # Reverse the DataFrame here
                     sender_name = row['sender_name']
-                    sender_id = row['sender_id']  # This will change with each message
-                    phone_number = row['phone_number']
-
-                    # Get response from the chat model
-                    response_content = get_chatmodel_response(message_text)
-
-                    # Check for drug word detected in the response
-                    if "drug word detected" in response_content and "none" not in response_content:
-                        drug_word = response_content.split("drug word detected: ")[1].strip()
-                        drug_count += 1
-                        drug_messages.append({
-                            "sender_name": sender_name,
-                            "sender_id": sender_id,
-                            "phone_number": phone_number,
-                            "message_text": message_text,
-                            "drug_word": drug_word
-                        })
-                        # Aggregate data by user
-                        if sender_name not in user_data:
-                            user_data[sender_name] = {
+                    message_text = row['message_text']
+                    # Display each message with its corresponding sender name
+                    st_message(f"{sender_name}: {message_text}", is_user=False, key=f"telegram_message_{idx}")
+
+                if st.button("Analyze 🚨"):
+                    # Initialize count and list for drug-related messages
+                    drug_count = 0
+                    drug_messages = []
+                    user_data = {}  # Initialize user data dictionary
+
+                    # Analyze each message for drug-related content
+                    for idx, row in messages_df.iterrows():
+                        message_text = row['message_text']
+                        sender_name = row['sender_name']
+                        sender_id = row['sender_id']  # This will change with each message
+                        phone_number = row['phone_number']
+
+                        # Get response from the chat model
+                        response_content = get_chatmodel_response(message_text)
+
+                        # Check for drug word detected in the response
+                        if "drug word detected" in response_content and "none" not in response_content:
+                            drug_word = response_content.split("drug word detected: ")[1].strip()
+                            drug_count += 1
+                            drug_messages.append({
+                                "sender_name": sender_name,
+                                "sender_id": sender_id,
                                 "phone_number": phone_number,
-                                "sender_id": sender_id,  # Store the sender_id here
-                                "message_count": 0,
-                                "drug_words": []
-                            }
-                        user_data[sender_name]["message_count"] += 1
-                        user_data[sender_name]["drug_words"].append(drug_word)
-
-                # Display statistics
-                st.subheader("Analysis Results 📊")
-                st.write(f"Total drug-related messages detected: {drug_count}")
-
-                if drug_count > 0:
-                    # Prepare data for visualization
-                    user_names = list(user_data.keys())
-                    message_counts = [data["message_count"] for data in user_data.values()]
-                    phone_numbers = [data["phone_number"] for data in user_data.values()]
-                    sender_ids = [data["sender_id"] for data in user_data.values()]  # Use stored sender_id
-
-                    # 1. Bar chart: Messages per user
-                    st.markdown("### Number of Messages per User 📊")
-                    fig = px.bar(
-                        x=user_names,
-                        y=message_counts,
-                        labels={'x': 'User Name', 'y': 'Message Count'},
-                        title="Messages Detected per User"
-                    )
-                    st.plotly_chart(fig)
-
-                    # 2. Pie chart: Distribution of drug-related messages
-                    st.markdown("### Drug Distribution Among Users 🍰")
-                    drugs_detected = [drug for user in user_data.values() for drug in user["drug_words"]]
-                    fig = px.pie(
-                        names=drugs_detected,
-                        title="Distribution of Detected Drugs"
-                    )
-                    st.plotly_chart(fig)
-
-                    # 3. Display user details in a table
-                    st.markdown("### User Details Table 📋")
-                    user_df = pd.DataFrame({
-                        "User Name": user_names,
-                        "Phone Number": phone_numbers,
-                        "Sender ID": sender_ids,  # Display the correct sender ID
-                        "Messages Detected": message_counts
-                    })
-                    st.dataframe(user_df)
-
-                    # Optionally: Link to the statistics page
-                    st.markdown("[View Statistics Page](#)")
-                else:
-                    st.write("No drug-related messages detected.")
+                                "message_text": message_text,
+                                "drug_word": drug_word
+                            })
+                            # Aggregate data by user
+                            if sender_name not in user_data:
+                                user_data[sender_name] = {
+                                    "phone_number": phone_number,
+                                    "sender_id": sender_id,  # Store the sender_id here
+                                    "message_count": 0,
+                                    "drug_words": []
+                                }
+                            user_data[sender_name]["message_count"] += 1
+                            user_data[sender_name]["drug_words"].append(drug_word)
+
+                    # Display statistics
+                    st.subheader("Analysis Results 📊")
+                    st.write(f"Total drug-related messages detected: {drug_count}")
+
+                    if drug_count > 0:
+                        # Prepare data for visualization
+                        user_names = list(user_data.keys())
+                        message_counts = [data["message_count"] for data in user_data.values()]
+                        phone_numbers = [data["phone_number"] for data in user_data.values()]
+                        sender_ids = [data["sender_id"] for data in user_data.values()]  # Use stored sender_id
+
+                        # 1. Bar chart: Messages per user
+                        st.markdown("### Number of Messages per User 📊")
+                        fig = px.bar(
+                            x=user_names,
+                            y=message_counts,
+                            labels={'x': 'User Name', 'y': 'Message Count'},
+                            title="Messages Detected per User"
+                        )
+                        st.plotly_chart(fig)
+
+                        # 2. Pie chart: Distribution of drug-related messages
+                        st.markdown("### Drug Distribution Among Users 🍰")
+                        drugs_detected = [drug for user in user_data.values() for drug in user["drug_words"]]
+                        fig = px.pie(
+                            names=drugs_detected,
+                            title="Distribution of Detected Drugs"
+                        )
+                        st.plotly_chart(fig)
+
+                        # 3. Display user details in a table
+                        st.markdown("### User Details Table 📋")
+                        user_df = pd.DataFrame({
+                            "User Name": user_names,
+                            "Phone Number": phone_numbers,
+                            "Sender ID": sender_ids,  # Display the correct sender ID
+                            "Messages Detected": message_counts
+                        })
+                        st.dataframe(user_df)
 
+                        # Optionally: Link to the statistics page
+                        st.markdown("[View Statistics Page](#)")
+                    else:
+                        st.write("No drug-related messages detected.")
 
-        else:
-            # Display chat messages for other platforms with unique keys
-            for idx, msg in enumerate(st.session_state.messages):
-                st_message(msg["message"], is_user=msg["is_user"], key=f"message_{idx}")
 
-            # Input for user query
-            input_text = st.text_input("Enter your text", key="user_input")
+            else:
+                # Display chat messages for other platforms with unique keys
+                for idx, msg in enumerate(st.session_state.messages):
+                    st_message(msg["message"], is_user=msg["is_user"], key=f"message_{idx}")
+
+                # Input for user query
+                input_text = st.text_input("Enter your text", key="user_input")
 
-            if st.button("Send"):
-                if input_text:
-                    # Append the user's message to session state
-                    st.session_state.messages.append({"message": input_text, "is_user": True})
+                if st.button("Send"):
+                    if input_text:
+                        # Append the user's message to session state
+                        st.session_state.messages.append({"message": input_text, "is_user": True})
 
-                    # Get the response from the model
-                    response = get_chatmodel_response(input_text)
+                        # Get the response from the model
+                        response = get_chatmodel_response(input_text)
 
-                    # Append the response from the model
-                    st.session_state.messages.append({"message": response, "is_user": False})
+                        # Append the response from the model
+                        st.session_state.messages.append({"message": response, "is_user": False})
 
-                    # Rerun to refresh the UI with new messages
-                    st.rerun()
-                else:
-                    st.warning("Please enter a message.")
+                        # Rerun to refresh the UI with new messages
+                        st.rerun()
+                    else:
+                        st.warning("Please enter a message.")
 
-    # Call the chatbot function
-    traffitrack_chatbot()
+        # Call the chatbot function
+        traffitrack_chatbot()
 
 
 # elif selected == "Statistics":
@@ -513,4 +516,4 @@ st.markdown(f"""
         padding: 10px;
     }}
     </style>
-""", unsafe_allow_html=True)
+""", unsafe_allow_html=True)