streamlit_app.py

"""Streamlit entrypoint for AgriPredict (refactored).

Run with: `streamlit run streamlit_app.py` from project root.
"""
import streamlit as st
from datetime import datetime, timedelta
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import os
from dotenv import load_dotenv

from src.agri_predict import (
    fetch_and_process_data,
    fetch_and_store_data,
    preprocess_data,
    train_and_forecast,
    forecast,
    collection_to_dataframe,
    get_dataframe_from_collection,
)
from src.agri_predict.constants import state_market_dict
from src.agri_predict.utils import authenticate_user
from src.agri_predict.config import get_collections

# Load environment variables
load_dotenv()
IS_PROD = os.getenv("PROD", "False").lower() == "true"


st.set_page_config(layout="wide")


def load_all_data(_collection):
    """Load all data from MongoDB collection."""
    data = list(_collection.find({}))
    if not data:
        return pd.DataFrame()
    df = pd.DataFrame(data)
    
    # Drop MongoDB _id field
    if '_id' in df.columns:
        df = df.drop(columns=['_id'])
    
    # Convert data types
    if 'Reported Date' in df.columns:
        df['Reported Date'] = pd.to_datetime(df['Reported Date'])
    if 'Modal Price (Rs./Quintal)' in df.columns:
        df['Modal Price (Rs./Quintal)'] = pd.to_numeric(df['Modal Price (Rs./Quintal)'], errors='coerce')
    if 'Arrivals (Tonnes)' in df.columns:
        df['Arrivals (Tonnes)'] = pd.to_numeric(df['Arrivals (Tonnes)'], errors='coerce')
    
    return df


def get_filtered_data(_collection, state=None, market=None, days=30):
    """Get filtered data based on parameters."""
    query_filter = {"Reported Date": {"$gte": datetime.now() - timedelta(days=days)}}
    if state and state != 'India':
        query_filter["State Name"] = state
    if market:
        query_filter["Market Name"] = market
    
    data = list(_collection.find(query_filter))
    if not data:
        return pd.DataFrame()
    
    df = pd.DataFrame(data)
    
    # Drop MongoDB _id field
    if '_id' in df.columns:
        df = df.drop(columns=['_id'])
    
    # Convert data types
    if 'Reported Date' in df.columns:
        df['Reported Date'] = pd.to_datetime(df['Reported Date'])
    if 'Modal Price (Rs./Quintal)' in df.columns:
        df['Modal Price (Rs./Quintal)'] = pd.to_numeric(df['Modal Price (Rs./Quintal)'], errors='coerce')
    if 'Arrivals (Tonnes)' in df.columns:
        df['Arrivals (Tonnes)'] = pd.to_numeric(df['Arrivals (Tonnes)'], errors='coerce')
    
    return df


st.markdown("""
    <style>
        .main { 
            max-width: 1200px; 
            margin: 0 auto;
            padding: 2rem;
        }
        .block-container {
            max-width: 1200px;
            padding-left: 5rem;
            padding-right: 5rem;
        }
        h1 { color: #4CAF50; font-family: 'Arial Black', sans-serif; }
        .stButton>button { background-color: #4CAF50; color: white; }
    </style>
""", unsafe_allow_html=True)

if 'authenticated' not in st.session_state:
    st.session_state.authenticated = False

if st.session_state.authenticated:
    # Get collections after authentication
    try:
        cols = get_collections()
    except Exception as exc:
        st.error(f"Configuration error: {exc}")
        st.stop()
    
    collection = cols['collection']
    impExp = cols['impExp']
    
    st.title("🌾 AgriPredict Dashboard")
    if st.button("Get Live Data Feed"):
        fetch_and_store_data()

    view_mode = st.radio("View Mode", ["Statistics", "Plots", "Predictions", "Exim"], horizontal=True, label_visibility="collapsed")

    if view_mode == "Plots":
        st.sidebar.header("Filters")
        selected_period = st.sidebar.selectbox("Select Time Period", ["2 Weeks", "1 Month", "3 Months", "1 Year", "5 Years"], index=1)
        period_mapping = {"2 Weeks": 14, "1 Month": 30, "3 Months": 90, "1 Year": 365, "2 Years": 730, "5 Years": 1825}
        st.session_state.selected_period = period_mapping[selected_period]

        state_options = list(state_market_dict.keys()) + ['India']
        selected_state = st.sidebar.selectbox("Select", state_options)

        market_wise = False
        if selected_state != 'India':
            market_wise = st.sidebar.checkbox("Market Wise Analysis")
            if market_wise:
                markets = state_market_dict.get(selected_state, [])
                selected_market = st.sidebar.selectbox("Select Market", markets)
                query_filter = {"State Name": selected_state, "Market Name": selected_market}
            else:
                query_filter = {"State Name": selected_state}
        else:
            query_filter = {}

        data_type = st.sidebar.radio("Select Data Type", ["Price", "Volume", "Both"])

        if st.sidebar.button("✨ Let's go!"):
            try:
                # Load data
                state_param = selected_state if selected_state != 'India' else None
                market_param = selected_market if market_wise else None
                
                df = get_filtered_data(collection, state_param, market_param, st.session_state.selected_period)
                
                if not df.empty:
                    # Group by date and aggregate
                    df_grouped = df.groupby('Reported Date', as_index=False).agg({
                        'Arrivals (Tonnes)': 'sum', 
                        'Modal Price (Rs./Quintal)': 'mean'
                    })
                    
                    # Create complete date range and fill gaps
                    date_range = pd.date_range(
                        start=df_grouped['Reported Date'].min(),
                        end=df_grouped['Reported Date'].max(),
                        freq='D'
                    )
                    df_grouped = df_grouped.set_index('Reported Date').reindex(date_range).rename_axis('Reported Date').reset_index()
                    
                    # Fill missing values using the working method
                    df_grouped['Arrivals (Tonnes)'] = df_grouped['Arrivals (Tonnes)'].ffill().bfill()
                    df_grouped['Modal Price (Rs./Quintal)'] = df_grouped['Modal Price (Rs./Quintal)'].ffill().bfill()
                    
                    st.subheader(f"📈 Trends for {selected_state} ({'Market: ' + selected_market if market_wise else 'State'})")
                    
                    if data_type == "Both":
                        # Min-Max Scaling
                        scaler = MinMaxScaler()
                        df_grouped[['Scaled Price', 'Scaled Arrivals']] = scaler.fit_transform(
                            df_grouped[['Modal Price (Rs./Quintal)', 'Arrivals (Tonnes)']]
                        )

                        import plotly.graph_objects as go
                        fig = go.Figure()

                        fig.add_trace(go.Scatter(
                            x=df_grouped['Reported Date'],
                            y=df_grouped['Scaled Price'],
                            mode='lines',
                            name='Scaled Price',
                            line=dict(width=1, color='green'),
                            text=df_grouped['Modal Price (Rs./Quintal)'],
                            hovertemplate='Date: %{x}<br>Scaled Price: %{y:.2f}<br>Actual Price: %{text:.2f}<extra></extra>'
                        ))

                        fig.add_trace(go.Scatter(
                            x=df_grouped['Reported Date'],
                            y=df_grouped['Scaled Arrivals'],
                            mode='lines',
                            name='Scaled Arrivals',
                            line=dict(width=1, color='blue'),
                            text=df_grouped['Arrivals (Tonnes)'],
                            hovertemplate='Date: %{x}<br>Scaled Arrivals: %{y:.2f}<br>Actual Arrivals: %{text:.2f}<extra></extra>'
                        ))

                        fig.update_layout(
                            title="Price and Arrivals Trend",
                            xaxis_title='Date',
                            yaxis_title='Scaled Values',
                            template='plotly_white'
                        )
                        st.plotly_chart(fig, use_container_width=True)
                    elif data_type == "Price":
                        # Plot Modal Price
                        import plotly.graph_objects as go
                        fig = go.Figure()
                        fig.add_trace(go.Scatter(
                            x=df_grouped['Reported Date'],
                            y=df_grouped['Modal Price (Rs./Quintal)'],
                            mode='lines',
                            name='Modal Price',
                            line=dict(width=1, color='green')
                        ))
                        fig.update_layout(title="Modal Price Trend", xaxis_title='Date', yaxis_title='Price (/Quintall)', template='plotly_white')
                        st.plotly_chart(fig, use_container_width=True)
                    elif data_type == "Volume":
                        # Plot Arrivals (Tonnes)
                        import plotly.graph_objects as go
                        fig = go.Figure()
                        fig.add_trace(go.Scatter(
                            x=df_grouped['Reported Date'],
                            y=df_grouped['Arrivals (Tonnes)'],
                            mode='lines',
                            name='Arrivals',
                            line=dict(width=1, color='blue')
                        ))
                        fig.update_layout(title="Arrivals Trend", xaxis_title='Date', yaxis_title='Volume (in Tonnes)', template='plotly_white')
                        st.plotly_chart(fig, use_container_width=True)
                else:
                    st.warning("⚠️ No data found for the selected filters.")
            except Exception as e:
                st.error(f"❌ Error fetching data: {e}")

    elif view_mode == "Predictions":
        st.subheader("📊 Model Analysis")
        sub_option = st.radio("Select one of the following", ["India", "States", "Market"], horizontal=True)
        sub_timeline = st.radio("Select one of the following horizons", ["14 days", "1 month", "3 month"], horizontal=True)
        if sub_option == "States":
            states = ["Karnataka", "Madhya Pradesh", "Gujarat", "Uttar Pradesh", "Telangana"]
            selected_state = st.selectbox("Select State for Model Training", states)
            filter_key = f"state_{selected_state}"
            if not IS_PROD and st.button("Train and Forecast"):
                query_filter = {"State Name": selected_state}
                df = fetch_and_process_data(query_filter)
                if df is not None:
                    if sub_timeline == "14 days":
                        train_and_forecast(df, filter_key, 14)
                    elif sub_timeline == "1 month":
                        train_and_forecast(df, filter_key, 30)
                    else:
                        train_and_forecast(df, filter_key, 90)
                else:
                    st.error("❌ No data available for the selected state.")
            if st.button("Forecast"):
                query_filter = {"State Name": selected_state}
                df = fetch_and_process_data(query_filter)
                if df is not None:
                    if sub_timeline == "14 days":
                        forecast(df, filter_key, 14)
                    elif sub_timeline == "1 month":
                        forecast(df, filter_key, 30)
                    else:
                        forecast(df, filter_key, 90)
                else:
                    st.error("❌ No data available for the selected state.")
        elif sub_option == "Market":
            market_options = ["Rajkot", "Gondal", "Kalburgi", "Amreli"]
            selected_market = st.selectbox("Select Market for Model Training", market_options)
            filter_key = f"market_{selected_market}"
            if not IS_PROD and st.button("Train and Forecast"):
                query_filter = {"Market Name": selected_market}
                df = fetch_and_process_data(query_filter)
                if df is not None:
                    if sub_timeline == "14 days":
                        train_and_forecast(df, filter_key, 14)
                    elif sub_timeline == "1 month":
                        train_and_forecast(df, filter_key, 30)
                    else:
                        train_and_forecast(df, filter_key, 90)
                else:
                    st.error("❌ No data available for the selected market.")
            elif st.button("Forecast"):
                query_filter = {"Market Name": selected_market}
                df = fetch_and_process_data(query_filter)
                if df is not None:
                    if sub_timeline == "14 days":
                        forecast(df, filter_key, 14)
                    elif sub_timeline == "1 month":
                        forecast(df, filter_key, 30)
                    else:
                        forecast(df, filter_key, 90)
                else:
                    st.error("❌ No data available for the selected market.")
        elif sub_option == "India":
            df = collection_to_dataframe(impExp)
            if not IS_PROD and st.button("Train and Forecast"):
                query_filter = {}
                df = fetch_and_process_data(query_filter)
                if df is not None:
                    if sub_timeline == "14 days":
                        train_and_forecast(df, "India", 14)
                    elif sub_timeline == "1 month":
                        train_and_forecast(df, "India", 30)
                    else:
                        train_and_forecast(df, "India", 90)
                else:
                    st.error("❌ No data available for forecasting.")
            if st.button("Forecast"):
                query_filter = {}
                df = fetch_and_process_data(query_filter)
                if df is not None:
                    if sub_timeline == "14 days":
                        forecast(df, "India", 14)
                    elif sub_timeline == "1 month":
                        forecast(df, "India", 30)
                    else:
                        forecast(df, "India", 90)
                else:
                    st.error("❌ No data available for forecasting.")

    elif view_mode == "Statistics":
        # Use cached data loading
        df = load_all_data(collection)
        if not df.empty:
            from src.agri_predict.plotting import display_statistics
            display_statistics(df)
        else:
            st.warning("No data available to display statistics.")
    elif view_mode == "Exim":
        df = collection_to_dataframe(impExp)
        plot_option = st.radio("Select the data to visualize:", ["Import Price", "Import Quantity", "Export Price", "Export Quantity"], horizontal=True)
        time_period = st.selectbox("Select time period:", ["1 Month", "6 Months", "1 Year", "2 Years"])
        df["Reported Date"] = pd.to_datetime(df["Reported Date"], format="%Y-%m-%d")
        if time_period == "1 Month":
            start_date = pd.Timestamp.now() - pd.DateOffset(months=1)
        elif time_period == "6 Months":
            start_date = pd.Timestamp.now() - pd.DateOffset(months=6)
        elif time_period == "1 Year":
            start_date = pd.Timestamp.now() - pd.DateOffset(years=1)
        else:
            start_date = pd.Timestamp.now() - pd.DateOffset(years=2)
        filtered_df = df[df["Reported Date"] >= start_date]
        if plot_option == "Import Price":
            grouped_df = filtered_df.groupby("Reported Date", as_index=False)["VALUE_IMPORT"].mean().rename(columns={"VALUE_IMPORT": "Average Import Price"})
            y_axis_label = "Average Import Price (Rs.)"
        elif plot_option == "Import Quantity":
            grouped_df = filtered_df.groupby("Reported Date", as_index=False)["QUANTITY_IMPORT"].sum().rename(columns={"QUANTITY_IMPORT": "Total Import Quantity"})
            y_axis_label = "Total Import Quantity (Tonnes)"
        elif plot_option == "Export Price":
            grouped_df = filtered_df.groupby("Reported Date", as_index=False)["VALUE_EXPORT"].mean().rename(columns={"VALUE_EXPORT": "Average Export Price"})
            y_axis_label = "Average Export Price (Rs.)"
        else:
            grouped_df = filtered_df.groupby("Reported Date", as_index=False)["QUANTITY_IMPORT"].sum().rename(columns={"QUANTITY_IMPORT": "Total Export Quantity"})
            y_axis_label = "Total Export Quantity (Tonnes)"
        import plotly.express as px
        fig = px.line(grouped_df, x="Reported Date", y=grouped_df.columns[1], title=f"{plot_option} Over Time", labels={"Reported Date": "Date", grouped_df.columns[1]: y_axis_label})
        st.plotly_chart(fig)

else:
    with st.form("login_form"):
        st.subheader("Please log in")
        username = st.text_input("Username")
        password = st.text_input("Password", type="password")
        login_button = st.form_submit_button("Login")
        if login_button:
            # Get collections for authentication
            try:
                cols = get_collections()
                users_collection = cols['users_collection']
            except Exception as exc:
                st.error(f"Database connection error: {exc}")
                st.stop()
            
            if authenticate_user(username, password, users_collection):
                st.session_state.authenticated = True
                st.session_state['username'] = username
                st.write("Login successful!")
                st.rerun()
            else:
                st.error("Invalid username or password")