app.py

import streamlit as st 
import pandas as pd
from datetime import datetime
import folium
from streamlit_folium import folium_static
import groq

# Load bus data
data_path = r"C:\Users\Muthuraja\OneDrive\Attachments\Desktop\second\pdp.csv"
df = pd.read_csv(data_path)

# Dummy user credentials
USER_CREDENTIALS = {
    "Muthuraja":"virat",
    "Praveen":"dhoni",
    "Pandi":"kabadi",
    "admin": "password123",
    "user": "buspass2025"
}

# Groq API Key
GROQ_API_KEY = "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"
groq.api_key = GROQ_API_KEY

# User login
def authenticate(username, password):
    if username in USER_CREDENTIALS and USER_CREDENTIALS[username] == password:
        return True
    return False

# Generate bus prediction using Groq API
def predict_bus_status(bus_number, city, area):
    prompt = f"Predict the status and arrival time for bus {bus_number} in {city}, {area}."
    try:
        client = groq.Client(api_key=GROQ_API_KEY)
        response = client.chat.completions.create(
            model="llama3-70b-8192",  # Correct model name for Groq  # Use Groq's model, adjust if needed
            messages=[{"role": "system", "content": prompt}]
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error: {e}"

# Plot bus locations using Folium with real coordinates
def plot_bus_map(area_df):
    if area_df.empty:
        st.warning("No bus data available for this area.")
        return

    if "latitude" not in area_df.columns or "longitude" not in area_df.columns:
        st.error("Latitude and longitude columns missing. Please update CSV.")
        return

    center_lat = area_df["latitude"].mean()
    center_lon = area_df["longitude"].mean()
    
    m = folium.Map(location=[center_lat, center_lon], zoom_start=12)

    for _, row in area_df.iterrows():
        folium.Marker(
            location=[row["latitude"], row["longitude"]],
            popup=f"{row['bus_number']} - {row['bus_route']}\nArrival: {row['arrival_time']}\nStatus: {row['status']}",
            icon=folium.Icon(color="blue" if row["status"].lower() == "on time" else "red")
        ).add_to(m)

    folium_static(m)

# Streamlit UI
st.title("🚍 Tamil Nadu Bus Tracking & Prediction System")

# Login form
if "authenticated" not in st.session_state:
    st.session_state.authenticated = False

if not st.session_state.authenticated:
    username = st.text_input("Username")
    password = st.text_input("Password", type="password")
    if st.button("Login"):
        if authenticate(username, password):
            st.session_state.authenticated = True
            st.success("Login successful!")
        else:
            st.error("Invalid username or password")

if st.session_state.authenticated:
    city = st.selectbox("Select City", df["city"].unique())
    area = st.selectbox("Select Area", df[df["city"] == city]["area"].unique())

    filtered_df = df[(df["city"] == city) & (df["area"] == area)]
    
    st.subheader(f"🚌 Bus Details for {city}, {area}")
    st.dataframe(filtered_df)

    st.subheader("🗺️ Bus Map View")
    plot_bus_map(filtered_df)

    def predict_next_bus(area_df):
        now = datetime.now()
        upcoming_buses = area_df[area_df["arrival_time"] > now.strftime("%Y-%m-%d %H:%M:%S")]
        if not upcoming_buses.empty:
            next_bus = upcoming_buses.iloc[0]
            prediction = predict_bus_status(next_bus['bus_number'], city, area)
            return f"🚏 Next bus: {next_bus['bus_number']} arriving at {next_bus['arrival_time']}\n🔮 Prediction: {prediction}"
        return "⚠️ No upcoming buses available."

    prediction = predict_next_bus(filtered_df)
    st.success(prediction)

    if st.button("Logout"):
        st.session_state.authenticated = False
        st.experimental_rerun()