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| import streamlit as st | |
| import plotly.graph_objects as go | |
| from pymongo import MongoClient | |
| from datetime import datetime, timedelta | |
| import pandas as pd | |
| from sklearn.preprocessing import MinMaxScaler | |
| import certifi | |
| import json | |
| import os | |
| mongo_uri = "mongodb+srv://Agripredict:TjXSvMhOis49qH8E@cluster0.gek7n.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0" | |
| if not mongo_uri: | |
| st.error("MongoDB URI is not set!") | |
| st.stop() | |
| else: | |
| # Connect to MongoDB with SSL certificate validation | |
| client = MongoClient(mongo_uri, tlsCAFile=certifi.where()) | |
| db = client["AgriPredict"] | |
| collection = db["WhiteSesame"] | |
| # CSS to increase the width of the container | |
| st.markdown(""" | |
| <style> | |
| /* Adjust the width of the main container */ | |
| .main { | |
| max-width: 1200px; /* Increase the width */ | |
| margin: 0 auto; /* Center the container */ | |
| } | |
| /* Main background */ | |
| body { | |
| background-color: #f9f9f9; | |
| } | |
| /* Title styling */ | |
| h1 { | |
| color: #4CAF50; | |
| font-family: 'Arial Black', sans-serif; | |
| } | |
| /* Buttons */ | |
| .stButton>button { | |
| background-color: #4CAF50; | |
| color: white; | |
| font-size: 14px; | |
| border-radius: 8px; | |
| padding: 10px 20px; | |
| margin: 5px; | |
| white-space: nowrap; | |
| } | |
| .stButton>button:hover { | |
| background-color: #45a049; | |
| } | |
| /* Selectbox styling */ | |
| .stSelectbox>div { | |
| padding: 10px; | |
| background-color: #ffffff; | |
| border: 1px solid #e6e6e6; | |
| border-radius: 8px; | |
| } | |
| /* Checkbox styling */ | |
| .stCheckbox>label { | |
| font-size: 14px; | |
| color: #555; | |
| } | |
| /* Containers */ | |
| .stContainer { | |
| border-radius: 12px; | |
| padding: 20px; | |
| background-color: #ffffff; | |
| box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); | |
| } | |
| /* Chart area */ | |
| .plotly-graph-div { | |
| border-radius: 12px; | |
| box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); | |
| } | |
| /* Footer */ | |
| footer { | |
| font-size: 12px; | |
| text-align: center; | |
| color: #888; | |
| padding: 10px; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| st.title("🌾 AgriPredict Dashboard") | |
| # Load the state-market dictionary from the JSON file | |
| with open('all_state_market_dict.json', 'r') as file: | |
| state_market_dict = json.load(file) | |
| # UI for Dashboard | |
| with st.container(): | |
| with st.expander("AgriPredict Dashboard", expanded=True): | |
| # Adjust the columns to fit more elements within the container | |
| col1, col2, col3, col4, col5, col6, col7 = st.columns([1.5, 1.5, 1.5, 1.5, 1.5, 3, 3]) | |
| # Buttons for periods | |
| with col1: | |
| if st.button('2W', key='2_weeks'): | |
| st.session_state.selected_period = 14 | |
| with col2: | |
| if st.button('1M', key='1_month'): | |
| st.session_state.selected_period = 30 | |
| with col3: | |
| if st.button('3M', key='3_months'): | |
| st.session_state.selected_period = 90 | |
| with col4: | |
| if st.button('1Y', key='1_year'): | |
| st.session_state.selected_period = 365 | |
| with col5: | |
| if st.button('5Y', key='5_year'): | |
| st.session_state.selected_period = 1825 | |
| # Dropdown for states | |
| with col6: | |
| states = list(state_market_dict.keys()) | |
| selected_state = st.selectbox( | |
| "Choose a state", | |
| states, | |
| key="state_selectbox", | |
| index=0 | |
| ) | |
| # Dropdown for selecting between Price, Volume, or Both | |
| with col7: | |
| data_type = st.selectbox( | |
| "Select Data Type", | |
| ["Price", "Volume", "Both"] | |
| ) | |
| # Checkbox for market-wise analysis | |
| st.write("") | |
| with st.container(): | |
| market_wise = st.checkbox("Market wise", key="market_checkbox") | |
| if market_wise: | |
| # Get markets for the selected state | |
| markets = state_market_dict.get(selected_state, []) | |
| selected_market = st.selectbox( | |
| "Choose a market", | |
| markets, | |
| key="market_selectbox", | |
| index=0 | |
| ) | |
| query_filter = {"state": selected_state, "Market Name": selected_market} | |
| else: | |
| query_filter = {"state": selected_state} | |
| # Add date filtering based on selected period | |
| if 'selected_period' in st.session_state: | |
| days_period = st.session_state.selected_period | |
| query_filter["Reported Date"] = { | |
| "$gte": datetime.now() - timedelta(days=days_period) | |
| } | |
| # Fetch data from MongoDB | |
| try: | |
| cursor = collection.find(query_filter) | |
| data = list(cursor) | |
| if data: | |
| # Convert MongoDB data to a DataFrame | |
| df = pd.DataFrame(data) | |
| df['Reported Date'] = pd.to_datetime(df['Reported Date']) | |
| # Group by Reported Date | |
| df_grouped = ( | |
| df.groupby('Reported Date', as_index=False) | |
| .agg({ | |
| 'Arrivals (Tonnes)': 'sum', | |
| 'Modal Price (Rs./Quintal)': 'mean' | |
| }) | |
| ) | |
| # Create a complete date range | |
| date_range = pd.date_range( | |
| start=df_grouped['Reported Date'].min(), | |
| end=df_grouped['Reported Date'].max() | |
| ) | |
| df_grouped = df_grouped.set_index('Reported Date').reindex(date_range).rename_axis('Reported Date').reset_index() | |
| # Fill missing values | |
| df_grouped['Arrivals (Tonnes)'] = df_grouped['Arrivals (Tonnes)'].fillna( | |
| method='ffill').fillna(method='bfill') | |
| df_grouped['Modal Price (Rs./Quintal)'] = df_grouped['Modal Price (Rs./Quintal)'].fillna( | |
| method='ffill').fillna(method='bfill') | |
| st.subheader(f"📈 Trend Graph 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)']] | |
| ) | |
| fig = go.Figure() | |
| # Plot Scaled Price with actual values on hover | |
| 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)'], # Actual Modal Price values | |
| hovertemplate='Date: %{x}<br>Scaled Price: %{y:.2f}<br>Actual Price: %{text:.2f}<extra></extra>' | |
| )) | |
| # Plot Scaled Arrivals with actual values on hover | |
| 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)'], # Actual Arrivals values | |
| 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) | |
| elif data_type == "Price": | |
| # Plot Modal Price | |
| 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', template='plotly_white') | |
| st.plotly_chart(fig) | |
| elif data_type == "Volume": | |
| # Plot Arrivals (Tonnes) | |
| 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', template='plotly_white') | |
| st.plotly_chart(fig) | |
| else: | |
| st.warning("⚠️ No relevant data found for the selected options.") | |
| else: | |
| st.warning("⚠️ No data found for the selected filters.") | |
| except Exception as e: | |
| st.error(f"❌ Error fetching data: {e}") |