Create app.py

app.py

@@ -0,0 +1,505 @@
+import streamlit as st
+from streamlit_chat import message as st_message
+from streamlit_option_menu import option_menu
+import os
+import plotly.express as px
+from io import StringIO
+from langchain.schema import HumanMessage, SystemMessage, AIMessage
+from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.prompts import PromptTemplate
+import warnings
+import time
+from sqlalchemy import create_engine, Column, Integer, String, Text, Table, MetaData
+from sqlalchemy.orm import sessionmaker
+import matplotlib.pyplot as plt
+from langchain_groq import ChatGroq
+import pandas as pd
+import numpy as np
+from dotenv import load_dotenv
+import re
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+load_dotenv()
+
+os.environ['GROQ_API_KEY'] = os.getenv("GROQ_API_KEY")
+llm = ChatGroq(model="llama-3.1-70b-versatile")
+
+
+# Streamlit page configuration
+st.set_page_config(
+    page_title="TraffiTrack", 
+    page_icon="", 
+    layout="wide", 
+    initial_sidebar_state="expanded", 
+)
+
+# Initialize session state for messages and banned users
+if 'messages' not in st.session_state:
+    st.session_state.messages = [{"message": "Hi! How can I assist you today?", "is_user": False}]
+if 'banned_users' not in st.session_state:
+    st.session_state.banned_users = []
+if 'flowmessages' not in st.session_state:
+    st.session_state.flowmessages = []
+
+
+# Function to handle registration
+def registration():
+    st.title("User Registration")
+
+    # Ensure session state is initialized
+    if "user_data" not in st.session_state:
+        st.session_state.user_data = []
+
+    name = st.text_input("Enter your name")
+    phone_number = st.text_input("Enter your phone number")
+
+    if st.button("Register"):
+        if name and phone_number:
+            # Append user data to session state as a dictionary
+            st.session_state.user_data.append({"name": name, "phone_number": phone_number})
+            st.success("Registration successful!")
+        else:
+            st.warning("Please fill in all fields.")
+
+
+# Function to simulate drug tracking data
+def generate_sample_data():
+    data = {
+        "Drug Name": ["MDMA", "LSD", "Mephedrone", "Cocaine", "Heroin"],
+        "Detected Instances": [10, 15, 7, 12, 5],
+        "Flagged Users": [5, 10, 4, 7, 3],
+        "IP Addresses": [3, 8, 2, 6, 2]
+    }
+    return pd.DataFrame(data)
+
+# Function to check for drug-related content and extract info
+def check_for_drug_content(input_text):
+    drug_keywords = ["MDMA", "LSD", "Mephedrone", "Cocaine", "Heroin"]
+    pattern = r'(\+?\d{1,3}[-. ]?)?\(?\d{1,4}?\)?[-. ]?\d{1,4}[-. ]?\d{1,4}[-. ]?\d{1,9}'  # Regex for phone numbers
+    ip_pattern = r'(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)'  # Regex for IP addresses
+
+    found_drugs = [keyword for keyword in drug_keywords if keyword.lower() in input_text.lower()]
+    phone_numbers = re.findall(pattern, 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"},
+    }
+)
+
+
+# Function to get a response from the chat model
+def get_chatmodel_response(user_message):
+    # Ensure user_message is a string
+    if "user_data" in st.session_state and st.session_state.user_data:
+        user_info = st.session_state.user_data[-1]  # Get the most recent registered user
+        name = user_info.get("name", "Unknown")
+        phone_number = user_info.get("phone_number", "Unknown")
+    else:
+        name = "Unknown"
+        phone_number = "Unknown"
+
+#better the prompt more formatting
+#chat types -> human message
+#response -> no drug message detected
+#from registration
+#remove debugging
+
+    PROMPT_TEMPLATE = """
+    You are an expert assistant specializing in detecting drug-related messages for monitoring illegal drug activities. Your role is to analyze user messages carefully to identify mentions of illegal substances or any activity related to drug trafficking, sales, or use. Your task is not just to detect specific drug names but to understand the context of the conversation, even if indirect or slang terms are used.
+
+    Key substances and related slang to focus on:
+    - MDMA (Molly, Mandy)
+    - LSD (Acid, Tabs)
+    - Mephedrone (Meow Meow)
+    - Cocaine (Coke, Snow, Blow, White, Charlie)
+    - Heroin (Smack, H, Brown)
+    - Marijuana (Weed, Pot, Mary Jane, Ganja, Bud)
+    - Ecstasy (X, XTC, E)
+    - Crack (Rock, Freebase)
+
+    Common phrases to consider:
+    - “Looking for Molly” (MDMA reference)
+    - “I need some snow” (Cocaine reference)
+    - “Got any tabs?” (LSD reference)
+    - “Can you hook me up with some blow?” (Cocaine reference)
+    - “I don’t want to mess with Charlie” (Cocaine reference in a negative context)
+
+    Instructions:
+    1. If you detect any mention of the above substances, slang, or any activities related to drug sales, trafficking, or use, respond with a simple confirmation of detection, including the drug name or slang term detected.
+    2. Be aware of **negations** or sentences where the user may **refuse** the drugs (e.g., "I don’t want cocaine"). Do not flag these as positive detections.
+    3. **Do not** include the original user message in your response.
+    4. Ensure the format includes the following fields: sender's name, phone number, and the detected drug word/slang. However, if there is **no drug-related content**, respond with only the message "drug word detected: none."
+
+    Below is the required format for your response:
+
+    If a drug word is detected:
+            {name}
+            {phone_number}
+            drug word detected: {{drug_word_detected}}
+
+    If no drug word is detected:
+            drug word detected: none
+
+    Below is the user message
+
+    User message: {user_message}
+
+    Response: """
+
+
+
+    memory = ConversationBufferWindowMemory(k=5, return_messages=True)
+    user_message = str(user_message)
+
+    # Use the parameter user_message to format the prompt
+    formatted_prompt = PROMPT_TEMPLATE.format(
+        user_message=user_message,
+        name=name,
+        phone_number=phone_number
+    )
+    # Add the formatted prompt to the conversation history
+    st.session_state.flowmessages.append(HumanMessage(content=user_message))
+
+    # Generate a response from the model
+    response = llm([SystemMessage(content=formatted_prompt)])
+
+    # Ensure the response.content is a string
+    response_content = str(response.content)
+
+    # Add the AI response to the conversation history
+    st.session_state.flowmessages.append(AIMessage(content=response_content))
+
+
+    # Save the conversation context
+    memory.save_context({"input": user_message}, {"output": response_content})
+
+    return response_content
+
+
+    
+    # User input for query
+    
+    # Button to send the message
+    # if st.button("Send"):
+    #     if user_input:
+    #         response = get_chatmodel_response(user_input)
+    #         st.session_state.messages.append({"message": response, "is_user": False})
+    #         st.experimental_rerun()
+    #     else:
+    #         st.warning("Please enter a message.")
+    
+    # Display the conversation history
+    if "flowmessages" in st.session_state:
+        st.subheader("Chat")
+        for message in st.session_state.flowmessages:
+            if isinstance(message, HumanMessage):
+                st_message(message.content, is_user=True)
+            elif isinstance(message, AIMessage):
+                st_message(message.content, is_user=False)
+
+def display_home_info():
+    # Set background color
+    st.markdown(
+        """
+        <style>
+        .reportview-container {
+            background: #DEF9C4;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+    
+    # Title with emoji
+    st.title("🏠 Welcome to the Drug-Related Content Detector")
+
+    # Section for description
+    st.markdown(
+        """
+        <div style='background-color: #50B498; padding: 10px; border-radius: 5px;'>
+            <h3 style='color: white;'>Our software solution helps identify drug-related content across multiple platforms.</h3>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+    # Features list
+    st.write("### Features include:")
+    st.markdown(
+        """
+        <ul style='list-style-type: none;'>
+            <li>🌐 Real-time monitoring of messages.</li>
+            <li>🖼️ Detection of images and text related to drug trafficking.</li>
+            <li>📊 Comprehensive statistics and insights.</li>
+        </ul>
+        """,
+        unsafe_allow_html=True
+    )
+
+if selected == "Registration":
+    registration()
+
+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  # Initialize drug_count here
+                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']
+                    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] = {
+                                "phone_number": phone_number,
+                                "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:
+                    # st.write("Details of detected messages:")
+                    # for message in drug_messages:
+                    #     st.markdown(f"**Phone Number**: {message['phone_number']}  \
+                    #                 **Sender ID**: {message['sender_id']}  \
+                    #                 **Message**: {message['message_text']}  \
+                    #                 **Drug Detected**: {message['drug_word']}")
+                    
+                    # 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()]
+
+                    # 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. Horizontal bar chart: Number of drug-related messages per user
+                    st.markdown("### Drug-related Messages per User 📊")
+                    fig = px.bar(
+                        y=user_names, 
+                        x=message_counts, 
+                        orientation='h', 
+                        labels={'y': 'User Name', 'x': 'Drug-related Messages Count'}, 
+                        title="Drug-related Messages per User"
+                    )
+                    st.plotly_chart(fig)
+
+                    # 4. Display user details in a table
+                    st.markdown("### User Details Table 📋")
+                    user_df = pd.DataFrame({
+                        "User Name": user_names,
+                        "Phone Number": phone_numbers,
+                        "Message_id" : 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")
+
+            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)
+
+                    # 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.experimental_rerun()
+                else:
+                    st.warning("Please enter a message.")
+
+    # Call the chatbot function
+    traffitrack_chatbot()
+
+
+# elif selected == "Statistics":
+#     st.title('Drug Trafficking Statistics 📊')
+
+#     # Generate sample data
+#     data = generate_sample_data()
+
+#     # Display data
+#     st.subheader("Overview of Detected Drugs")
+#     st.dataframe(data)
+
+#     # Plotting the data
+#     st.subheader("Detected Instances of Drugs")
+#     fig, ax = plt.subplots(figsize=(8, 5))
+#     ax.bar(data["Drug Name"], data["Detected Instances"], color="#50B498")
+#     plt.title("Detected Instances of Drugs")
+#     plt.xlabel("Drug Name")
+#     plt.ylabel("Detected Instances")
+#     st.pyplot(fig)
+
+#     # Plotting flagged users
+#     st.subheader("Flagged Users")
+#     fig, ax = plt.subplots(figsize=(8, 5))
+#     ax.bar(data["Drug Name"], data["Flagged Users"], color="#468585")
+#     plt.title("Flagged Users")
+#     plt.xlabel("Drug Name")
+#     plt.ylabel("Flagged Users")
+#     st.pyplot(fig)
+
+#     # Plotting IP addresses
+#     st.subheader("Detected IP Addresses")
+#     fig, ax = plt.subplots(figsize=(8, 5))
+#     ax.bar(data["Drug Name"], data["IP Addresses"], color="#9CDBA6")
+#     plt.title("Detected IP Addresses")
+#     plt.xlabel("Drug Name")
+#     plt.ylabel("Detected IP Addresses")
+#     st.pyplot(fig)
+
+# Custom CSS for a better user interface
+st.markdown(f"""
+    <style>
+    .stApp {{
+        background-color: #DEF9C4;
+        color: #468585;
+    }}
+    .stButton>button {{
+        background-color: #50B498; 
+        color: #ffffff; 
+        border: none;
+        border-radius: 8px;
+        font-size: 16px;
+        padding: 10px 20px;
+        cursor: pointer;
+    }}
+    .stButton>button:hover {{
+        background-color: #9CDBA6;
+    }}
+    .stTextInput>input {{
+        background-color: #468585; 
+        color: #ffffff;
+        border: 2px solid #50B498;
+        border-radius: 8px;
+        padding: 10px;
+        font-size: 16px;
+    }}
+    h1, h2, h3 {{
+        color: #50B498;
+    }}
+    .stDataFrame {{
+        background-color: #ffffff;
+        color: #000000;
+        border-radius: 10px;
+        padding: 10px;
+    }}
+    </style>
+""", unsafe_allow_html=True)